Chesapeake Bay Climate Impacts Summary and Outlook

Mid-Atlantic Regional Climate Impacts Summary and Outlook: Winter 2023–2024

Highlights

  • Average temperatures for the winter season were four to six degrees above normal for most of the region. The last time the Mid-Atlantic experienced a similarly warmer than normal season was last winter (December 2022–February 2023).
  • The eastern portion of the region experienced wetter than normal conditions, up to 200 percent of normal precipitation.
  • A January snowstorm ended record long streaks of days without an inch of snow for a few sites. Lynchburg, Virginia, had gone 728 consecutive days without at least an inch of snow.
  • Drought peaked for the region in early December, with 76 percent of Virginia and 44 percent of Maryland in drought on December 5. But, by the end of January, most of the drought in the region eased and Virginia became drought free for the first time since August 2022.

View all climate summaries

This summary focuses on weather and climate events in the Chesapeake Bay watershed and provides highlights from the greater Mid-Atlantic region. The winter season is defined as the months of December, January, and February. The MARISA region covers Maryland, Delaware, Virginia, Pennsylvania, and the portions of New York and West Virginia that fall within the boundaries of the Chesapeake Bay watershed, as shown in Figure 1 below. We refer to this region as the Mid-Atlantic region in the rest of the climate summary.

Figure 1. MARISA Mid-Atlantic Region

A map of the Mid-Atlantic regional highlighting the Chesapeake Bay watershed.

This map shows the “MARISA region”. The lightly shaded area shows the extent of the Chesapeake Bay Watershed.

Part 1: Significant Weather Events and Impacts

Winter Weather

Snowfall from a storm on January 15 and 16 brought an end to a record-long streak of days without an inch of snow for a few sites.1 Lynchburg, Virginia went 728 consecutive days (January 17, 2022 through January 14, 2024) without at least an inch of snow.2 Baltimore, Maryland, and Dulles Airport, Virginia, ended record long streaks of 716 days and 693 days respectively.3,4 The snow created hazardous travel conditions, with multiple storm-related traffic accidents and several deaths.5,6

Severe Weather

A storm from December 10 to 11 brought heavy rain and gusty winds to the watershed. The greatest rainfall totals approached four inches in portions of Maryland and Virginia, with coastal areas in these states recording wind gusts of 40 to 60 miles per hour (mph).7 The storm also produced some snowfall, including the first measurable snow of the season for the Washington, D.C. area. 8

From December 17 to 19 the watershed saw another round of heavy rain with gusty winds. Eastern portions of the region generally saw the greatest rainfall totals (3-7 inches).9,10 December 17 became the ninth wettest December day on record for Dulles Airport, Virginia, with 1.91 inches of precipitation.11 December 18 became the second wettest December day on record for Scranton, Pennsylvania, and Binghamton, New York, seeing 2.16 inches and 2.06 inches respectively.12 The heavy rain led to flooding, with water entering homes and swamping roadways, which resulted in some water rescues in Pennsylvania and Maryland.13,14,15 Some coastal areas experienced flooding due the combination of rain and high water levels.16 Additionally, wind gusts of 30 to 60 mph downed trees and wires, which blocked roads and caused power outages across the region.17,18 There was at least one storm-related fatality within the watershed.19

The watershed saw a third notable storm from December 27 to 28. While precipitation totals were generally 3 inches or less, several locations such as central Maryland experienced localized flooding that resulted in a few road closures and water rescues.20,21

On January 9 and 10 a storm brought mild temperatures, strong winds, and heavy precipitation to the watershed. The greatest precipitation totals, mostly in the form of rain, were up to four inches.22,23 The rain, combined with already saturated ground and elevated waterways, led to flooding in some locations.24 Storm reports noted road closures and multiple water rescues.25 Wind gusts of up to 70 mph downed trees and power lines, which blocked roads, damaged buildings, and caused power outages.26,27 The Chesapeake Bay Bridge was closed to traffic in both directions for several hours due to the high winds.28 Unusually high water levels were recorded along the Chesapeake Bay in Maryland, with the gauges at Annapolis, Baltimore, Cambridge, Chesapeake City, and Tolchester reaching one of their five highest water levels on record.29 Floodwaters inundated roads, buildings, and vehicles, causing damage and leading to business closures.30,31

On February 28, an Enhanced Fujita scale 1 (EF-1) tornado with estimated wind speeds of up to 100 mph touched down in Broome County, New York.32 This was the county's first winter tornado since records began in 1950.33 The tornado downed trees and power lines and damaged a home and other property. 34

Figure 2. The White House is covered in snow in Washington, D.C., January 19, 2024.

A view of snow-covered lawns and gardens surrounding the White House on a gray winter day in January, 2024.

SOURCE: REUTERS/Ken Cedeno

Drought

The U.S. Drought Monitor from December 5 showed 76 percent of Virginia in drought, including 45 percent of the state in severe drought.35 Meanwhile, drought covered 44 percent of Maryland and was present in portions of eastern West Virginia and south-central Pennsylvania.36,37 Wetter-than-normal conditions in the month of December erased drought from Maryland and south-central Pennsylvania and chipped away at drought in Virginia and eastern West Virginia.38 The January 2 U.S. Drought Monitor showed only 35 percent of Virginia in drought, including only four percent in severe drought.39

Figure 3. U.S. Drought Monitor for Virginia – December 2, 2023–January 30, 2024

Animated gif of the US Drought Monitor heat map for Virginia.

SOURCE: U.S. Drought Monitor

In early December, the Interstate Commission on the Potomac River Basin issued another special Water Supply Outlook due to the ongoing dry conditions in the basin; however, wet conditions during the rest of the month relieved drought-related concerns.40,41 Below normal or lower groundwater levels persisted in several areas including south-central Pennsylvania and central Maryland, with voluntary water conservation efforts encouraged in multiple locations.42 Water supply recovery varied across the region, with suppliers such as the York Water Company in Pennsylvania and the town of Strasburg, Virginia, lifting mandatory use restrictions but others, like the Hanover Borough Water Department in Pennsylvania, keeping them in place.43,44 In early December, the town of Culpeper, Virginia was expected to approve a budget of $1.5 million to connect additional reserve wells to its water system, which had been impacted by the drought.45

Abundant precipitation in January erased drought and abnormally dry conditions across most of the watershed, with Virginia becoming free of drought for the first time since August 2022.46,47 Only a sliver of abnormal dryness remained in central New York by the end of January. 48

While water supplies continued to recover, several Pennsylvania communities had water use restrictions in place or were asked to voluntarily conserve water.49 In Virginia, the rainfall increased soil moisture, but groundwater levels remained below average in some locations.50 A hydroelectric dam along the Shenandoah River in Virginia experienced operating issues due to extremes in streamflow, going from being able to operate because of drought conditions to having to reduce operations due to high streamflow.51

The watershed remained free of drought during February, with only a small area of abnormal dryness persisting in central New York throughout the month.52 As conditions continued to improve, some water suppliers in Pennsylvania and Virginia lifted mandatory water use restrictions.53,54

Fog

A large portion of the United States, including the Chesapeake Bay watershed, experienced an unusually foggy period from January 23 to 27 when warm, moist air from the Gulf of Mexico moved over cold, and in some places, snow-covered, ground.55 Foggy conditions on January 27 were thought to have contributed to a 43-vehicle pileup on the Chesapeake Bay Bridge that injured 13 people.56

Part 2: Seasonal Temperature and Precipitation

Temperature

Figure 4 shows the winter 2023-2024 average temperature compared with the climate normal, which is defined as the average winter temperature from 1991 to 2020.57 The figure shows that most of the region experienced temperatures at least 3 degrees F above normal, with most of Pennsylvania and New York experiencing temperatures more than 4 degrees F above normal. A few areas in New York even experienced temperatures greater than 6 degrees F above normal. Only a few small portions of southwest Virginia experienced temperatures within 2 degrees F of normal. Overall, this was a very warm winter season. The last time the Mid-Atlantic saw a season this much warmer than normal was during the last winter season (December 2022-February 2023).

Figure 4. December 1, 2023–February 29, 2024 Departure from Normal Temperature (degrees Fahrenheit)

A heat map showing departure from normal temperature in the Mid-Atlantic region from December 2023 to January, 2024

SOURCE: Northeast Regional Climate Center, 2023 (https://www.nrcc.cornell.edu). Used with permission.

NOTE: Normal temperature is based on the winter season's average temperature data from 1991–2020. Yellow, orange, and red indicate above-normal temperatures. Blue indicates below-normal temperatures. The boundaries of the Chesapeake Bay watershed are outlined in bold black. Average departure from normal temperature is based on a station's normal temperature for winter compared with the same station's winter 2022-2023 average temperature. Station-level departures from normal are spatially interpolated across the region. Both are produced by the Northeast Regional Climate Center. These can be found at http://www.rcc-acis.org/docs_gridded.html.

This winter ranked among the top 20 warmest on record for most of the sites in the Chesapeake Bay watershed (Table 1), including Binghamton, New York, and Scranton, Pennsylvania, which experienced their warmest and second warmest winters on record, respectively. These two sites had their fewest number of days with a low at or below freezing this winter (67 and 59 days, respectively).58 In fact, a few other sites saw fewer days than usual with a minimum temperature of 32 degrees F or lower. This included Washington, D.C., which only saw 28 days with lows at or below freezing, its second fewest number on record. 59

Additional temperature-related events are discussed in the Monthly Temperature Rankings section below.

Table 1. Winter Season (December–February) Temperature Rankings

Station Name Avg. Temp (degrees F) Normal Temp (degrees F) Rank (warmest)
Binghamton, NY 32.3 25.0 1
Williamsport, PA 36.3 30.2 2
Scranton, PA 35.5 30.5 2
Dulles Airport, VA 40.7 36.0 3
Charlottesville, VA 43.3 40.4 4
Harrisburg, PA 38.1 33.3 5
Salisbury, MD 42.5 38.7 5
Washington National, DC 43.2 39.7 6
Baltimore, MD 40.8 36.5 7
Richmond, VA 44.0 40.4 7
Martinsburg, WV 37.1 34.5 15
Norfolk, VA 46.6 44.2 17
Lynchburg, VA 41.1 37.9 20

SOURCE: Northeast Regional Climate Center, 2023 (https://www.nrcc.cornell.edu). Used with permission.

NOTE: In this table, "avg. temp" is the temperature average from the winter season, while the "normal temp" is the 30-year average (from 1991-2020) for winter temperatures.

Monthly Temperature Rankings

December 2023 ranked among the 20 warmest months of December on record for 12 sites in the watershed. For instance, Binghamton, New York, and Williamsport, Pennsylvania, experienced their second warmest Decembers, while Washington, D.C., had its fifth warmest December and Baltimore, Maryland, experienced its seventh warmest December.

January 2024 ranked among the 20 warmest months of January on record for five sites (Table 2). On January 26, Washington, D.C., and Dulles Airport, Virginia, experienced their warmest high temperatures for the month of January.60 Washington, D.C, reached a high of 80 degrees F for the first time in January since records began in 1872.61 Meanwhile, Dulles Airport's high of 79 degrees F beat its old record by four degrees.62 On the same day, Dulles also tied its sixth warmest low temperature for January at 56 degrees F.63

February 2024 ranked among the 20 warmest months of February on record for 11 sites (Table 2). Binghamton, New York, had a high of 64°F on February 27, tying as the site's fourth warmest high temperature for February.64

Table 2. Monthly Temperature Rankings

December Temperature Rankings (warmest)
Station Name Avg. Temp (degrees F) Normal Temp (degrees F) Rank (warmest)
Binghamton, NY 36.7 28.1 2
Williamsport, PA 39.7 32.8 2
Scranton, PA 39.5 33.3 3
Dulles Airport, VA 43.0 37.7 4
Washington National, DC 45.5 41.7 5
Harrisburg, PA 41.1 35.8 6
Baltimore, MD 43.7 38.6 7
Charlottesville, VA 44.8 41.5 8
Salisbury, MD 45.6 40.6 10
Richmond, VA 45.6 41.8 15
Martinsburg, WV 39.5 36.0 16
Norfolk, VA 49.1 46.1 17
January Temperature Rankings (warmest)
Station Name Avg. Temp (degrees F) Normal Temp (degrees F) Rank (warmest)
Binghamton, NY 28.0 22.5 10
Dulles Airport, VA 36.9 33.9 13
Williamsport, PA 32.3 27.7 17
Salisbury, MD 40.5 36.8 18
Harrisburg, PA 35.0 30.8 20
February Temperature Rankings (warmest)
Station Name Avg. Temp (degrees F) Normal Temp (degrees F) Rank (warmest)
Binghamton, NY 32.1 24.5 2
Williamsport, PA 36.8 30.1 3
Dulles Airport, VA 42.3 36.4 4
Washington National, DC 44.4 40.0 7
Scranton, PA 35.3 30.3 8
Harrisburg, PA 38.3 33.4 8
Charlottesville, VA 45.4 41.4 9
Richmond, VA 44.9 41.0 11
Martinsburg, WV 38.9 35.0 14
Lynchburg, PA 43.4 38.8 15
Baltimore, MD 41.1 36.6 20

SOURCE: Northeast Regional Climate Center, 2023 (https://www.nrcc.cornell.edu). Used with permission.

NOTE: In this table, "avg. temp" is the temperature average from the indicated month, while the "normal temp" is the 30-year average (from 1991-2020) for that month's temperatures.

Precipitation

Figure 5 shows how the total precipitation for December 1, 2023 through February 29, 2024 differed from normal, with normal being defined as the average winter precipitation from 1991–2020. Much of the Chesapeake Bay watershed received 150-200 percent of normal precipitation. West of the watershed, most locations experienced slightly more precipitation than normal (100-125 percent). However, some small areas in south-western Virginia, western West Virginia, and north-western Pennsylvania received 75-150 percent of normal precipitation.

Figure 5. December 1, 2023–February 29, 2024 Percentage of Normal Precipitation

A heat map showing departure from normal precipitation for the Mid-Atlantic region for December 2023 to January, 2024. Source: Northeast Regional Climate Center, 2023

SOURCE: Northeast Regional Climate Center, 2023 (http://www.nrcc.cornell.edu). Used with permission.

NOTE: Normal seasonal precipitation is based on precipitation data from 1991–2020. Brown shades indicate below normal seasonal precipitation. Green shades indicate above normal seasonal precipitation. The boundaries of the Chesapeake Bay watershed are outlined in bold black. Average departures from normal precipitation are based on a station's normal precipitation for winter compared with the same station's winter 2023-2024 average amount of precipitation. Station-level departures from normal are spatially interpolated across the region. Both are produced by the Northeast Regional Climate Center. These can be found at http://www.rcc-acis.org/docs_gridded.html.

Overall, winter 2023–24 was the wettest winter on record for Binghamton, New York; Richmond, Virginia; and Scranton, Pennsylvania. This winter ranked among the 20 wettest winters on record for multiple additional sites in the watershed, shown in Table 3 below.

Table 3. Winter Season (December–February) Precipitation Rankings

Station Name Precipitation (inches) Normal Precipitation (inches) Rank
Binghamton, NY 12.27 8.11 1
Richmond, VA 17.97 9.35 1
Scranton, PA 13.01 7.46 1
Dulles Airport, VA 13.20 8.85 3
Baltimore, MD 15.77 9.69 4
Williamsport, PA 13.44 8.54 4
Harrisburg, PA 13.34 9.05 7
Salisbury, MD 15.44 10.35 9
Lynchburg, VA 14.09 9.87 10
Washington National, DC 13.72 8.89 12
Charlottesville, VA 11.85 8.35 14
Norfolk, VA 12.57 9.59 18

SOURCE: Northeast Regional Climate Center, 2023 (https://www.nrcc.cornell.edu). Used with permission.

Monthly Precipitation Rankings

This December ranked among the 20 wettest months of December for 11 sites including Norfolk, Virginia, with its second wettest December, Binghamton, New York, with its third wettest December, and Washington, D.C., with its fourth wettest December. Richmond, Virginia, had its wettest December since records began there in 1887, accumulating 8.87 inches of precipitation, which is nearly 95% of its normal total winter precipitation.65

December 17 became the ninth wettest December day on record for Dulles Airport, Virginia, with 1.91 inches of precipitation.66 On December 18, Scranton, Pennsylvania, and Binghamton, New York, had their second wettest December day with 2.16 inches and 2.06 inches of precipitation, respectively.67

January 2024 ranked among the 20 wettest months of January on record for 12 sites (Table 4). January 9 ranked among the 10 wettest January days for multiple sites including Baltimore, Maryland; Washington, D.C.; Williamsport and Scranton, Pennsylvania; and Dulles Airport, Virginia.68

In February, Dulles Airport, Virginia, experienced its 12th driest February on record and no sites experienced precipitation that ranked within their top 20 wettest on record.

The full set of monthly rankings, locations, and amounts of precipitation are shown in Table 4.

Table 4. Monthly Precipitation Rankings

December Precipitation Rankings (driest)
Station Name Precipitation (inches) Normal Precipitation (inches) Rank (driest)
No sites experienced precipitation that ranked among the top 20 driest months of December on record.
December Precipitation Rankings (wettest)
Station Name Precipitation (inches) Normal Precipitation (inches) Rank (wettest)
Richmond, VA 8.87 3.51 1
Norfolk, VA 6.43 3.28 2
Scranton, PA 5.84 2.80 3
Binghamton, NY 5.99 3.08 3
Baltimore, MD 7.16 3.71 3
Washington National, DC 6.43 3.41 4
Salisbury, MD 8.07 3.59 5
Dulles Airport, VA 5.75 3.30 6
Williamsport, PA 5.19 3.27 10
Harrisburg, PA 5.16 3.43 11
Lynchburg, VA 5.04 3.50 17
January Precipitation Rankings (driest)
Station Name Precipitation (inches) Normal Precipitation (inches) Rank (driest)
No sites experienced precipitation that ranked among the top 20 driest months of January on record.
January Precipitation Rankings (wettest)
Station Name Precipitation (inches) Normal Precipitation (inches) Rank (wettest)
Scranton, PA 5.35 2.59 3
Dulles Airport, VA 6.00 2.94 3
Binghamton, NY 4.62 2.62 4
Baltimore, MD 6.77 3.08 5
Williamsport, PA 6.13 2.96 5
Harrisburg, PA 5.82 3.03 5
Charlottesville, VA 5.88 2.96 6
Washington National, DC 5.88 2.86 7
Lynchburg, VA 6.30 3.46 8
Richmond, VA 5.76 3.23 9
Martinsburg, WV 4.11 2.60 11
Salisbury, MD 5.44 3.51 12
February Precipitation Rankings (driest)
Station Name Precipitation (inches) Normal Precipitation (inches) Rank (driest)
Dulles Airport, VA 1.45 2.61 12
February Precipitation Rankings (wettest)
Station Name Precipitation (inches) Normal Precipitation (inches) Rank (wettest)

No sites experienced precipitation that ranked in their top 20 wettest Februarys on record

Source: Northeast Regional Climate Center, 2023 (https://www.nrcc.cornell.edu). Used with permission.

Snowfall

Figure 6 shows how the total snowfall for December 1, 2023 through February 29, 2024 differed from normal, with normal being defined as the average winter snowfall from 1991–2020. The majority of the region received less than normal snowfall, with the easternmost portions of Maryland and much of southern Virginia receiving under 25 percent of normal snowfall. Only two small portions of central and eastern Pennsylvania experienced more than 100 percent of their normal winter snowfall this season.

Figure 6. December 1, 2023 – February 29, 2024, Percentage of Normal Snowfall

A heat map showing departure from normal snowfall for the Mid-Atlantic region for December 2023 to January, 2024. Source: Northeast Regional Climate Center, 2023

SOURCE: Northeast Regional Climate Center, 2023 (http://www.nrcc.cornell.edu). Used with permission.

NOTE: Normal seasonal snowfall is based on snowfall data from 1991–2020. Browns indicate below-average seasonal snowfall, and greens indicate above-normal seasonal snowfall. Percentages of normal seasonal snowfall are based on station-specific normal seasonal snowfall for the winter compared to the same station's winter 2023–2024 seasonal snowfall. Station-level percentages of normal are then spatially interpolated to form the figure above.

Winter 2023–2024 ranked among the 20 least snowy winters for several sites including Binghamton, New York, with its seventh least snowy winter and Richmond, Virginia, with its 11th least snowy winter. It was also among the top 10 least snowy winter seasons on record for three other sites (Table 5).

Table 5. Winter Season (December-February) Snowfall Rankings

Station Name Snowfall (inches) Normal Snowfall (inches) Rank (least snowy)
Norfolk, VA T 5.8 2 (tied with 12 other years)
Binghamton, NY 36.4 58.4 7
Richmond, VA 0.7 7.7 11
Salisbury, MD 1.4 6.8 11
Lynchburg, VA 2.7 9.1 16
Station Name Snowfall (inches) Normal Snowfall (inches) Rank (snowiest)

No sites experienced snowfall that ranked in their top 20 snowiest winters on record.

Source: Northeast Regional Climate Center, 2023 (https://www.nrcc.cornell.edu). Used with permission.

There was no measurable snow at several sites in southern parts of the watershed during the month of December, which is somewhat typical. December tied multiple other years as the least snowy December on record at Norfolk, Virginia. Binghamton, New York, experienced its seventh least snowy December, while Williamsport, Pennsylvania, experienced its 16th least snowy December.

January 2024 ranked as the 13th least snowy January for Norfolk, Virginia, tying many other years.69 However, it was among the 20 snowiest months of January on record for a few sites such as Dulles Airport, Virginia, and Binghamton, New York.

February 2024 was the least snowy February on record for several sites including Binghamton, New York, and Norfolk, Lynchburg, and Richmond, Virginia, with all Virginia sites tying multiple other years. Salisbury, Maryland, had its seventh least snowy February, also tying several other years.

The full set of monthly rankings, locations, and amounts of snowfall are shown in Table 6.

Table 6. Monthly Snowfall Rankings

December Snowfall Rankings
Station Name Snowfall (inches) Normal Snowfall (inches) Rank (snowiest)
No sites received snowfall that ranked in the top 20 snowiest Decembers on record.
December Snowfall Rankings
Station Name Snowfall (inches) Normal Snowfall (inches) Rank (least snowy)
Norfolk, VA 0.0 1.1 1 (tied with 31 other years)
Harrisburg, PA T 4.4 2 (tied with 12 other years)
Baltimore, MD T 2.5 5 (tied with 21 other years)
Binghamton, NY 6.8 18.1 7
Salisbury, MD T 0.9 12 (tied with 39 other years)
Williamsport, PA 0.6 6.9 16
Lynchburg, VA T 2.0 20 (tied with 30 other years)
Scranton, PA 3.1 7.7 20
January Snowfall Rankings (snowiest)
Station Name Snowfall (inches) Normal Snowfall (inches) Rank (snowiest)
Binghamton, NY 25.4 20.6 16
Dulles Airport, VA 9.5 6.9 19
January Snowfall Rankings (least snowy)
Station Name Snowfall (inches) Normal Snowfall (inches) Rank (least snowy)
Norfolk, VA T 3.2 13 (tied with 34 other years)
February Snowfall Rankings (snowiest)
Station Name Snowfall (inches) Normal Snowfall (inches) Rank (snowiest)

No sites received snowfall that ranked in the top 20 snowiest months of February on record.
February Snowfall Rankings (least snowy)
Station Name Snowfall (inches) Normal Snowfall (inches) Rank (least snowy)
Binghamton, NY 4.2 19.7 1
Lynchburg, VA 0.0 3.6 1 (tied with 8 other years)
Norfolk, VA 0.0 1.5 1 (tied with 16 other years)
Richmond, VA 0.0 2.2 1 (tied with 10 other years)
Salisbury, MD T 3.4 7 (tied with 10 other years)

Source: Northeast Regional Climate Center, 2023 (https://www.nrcc.cornell.edu). Used with permission.

Part 3: Spring 2024 Outlook

Temperature and Precipitation

As of February 15, 2024 the NOAA Climate Prediction Center forecasts a 40-50 percent chance of above normal temperatures for the Mid-Atlantic region for West Virginia, Virginia, Maryland, Washington, D.C., Delaware, and the southern half of Pennsylvania and a 33-40 percent change of above normal temperatures for the northern half of Pennsylvania and New York for March, April, and May 2024.70 This indicates that the forecast is leaning towards having a warmer than normal spring season.71 The precipitation forecast shows a 33-40 percent chance of above normal precipitation in Pennsylvania and the northern portions of Maryland and West Virginia, and a 40-50 percent chance of above normal precipitation for most of Maryland, all of Washington D.C., Delaware, and Virginia for the spring season.72

Drought Incidence

The U.S. Seasonal Drought Outlook identifies how drought might change across the United States and categorizes areas by whether drought could develop or become more or less intense. As of January 31, 2024 the Outlook indicates that drought conditions are not expected in the Mid-Atlantic region in the 2024 spring season.73

Climate Circulation Patterns

NOAA's Climate Prediction Center, which monitors the likelihood of occurrence of El Niño and La Niña climate phenomena, has an El Niño advisory active as of February 8, 2024.74 They expect El Niño conditions to weaken during the spring with a 79 percent chance of transitioning to ENSO-neutral between April and June 2024 with a 55% chance of La Niña conditions developing during the sumer season.75

ENSO conditions are one of the factors taken into account in NOAA's long-term forecasts and seasonal outlooks such as the one included in this climate summary.76 However, other regional climate dynamics and natural climate variability also influence weather in the Mid-Atlantic. Additional information on La Niña and El Niño is available from the Pacific Marine Environmental Laboratory (La Niña, El Niño).

Part 4: A Primer on Climate Projections and Future Climate Scenarios

Climate models are physics-based computer simulations that represent Earth systems, such as the ocean and atmosphere, and help us see how different potential future states of the world may result in different climate-related outcomes and impacts.77 There are dozens of groups of scientists that have developed global climate models all over the world, leading to the use of tens of different models to study global climate change.78 Through the Coupled Model Intercomparison Project (CMIP) and in support of the Intergovernmental Panel on Climate Change (IPCC) assessments, every five years or so, global climate modeling groups come together under the leadership of the World Climate Research Programme (WCRP) and, among other modeling goals, agree to run their models with the same set of potential future scenarios as their inputs with the goal of compiling and comparing model results.79 Comparing many models run with the same inputs leads to a broader understanding of the range of future climate changes and makes it possible to provide information about their levels of confidence and uncertainty, which is important for users to be able to interpret and act on the climate projections.80

Projections of global climate change from CMIP efforts are utilized by a range of scientific, practitioner and policy stakeholders from academic papers examining future tropical cyclone activity and intensity to designing infrastructure to withstand future climate conditions. But doing so requires an understanding of the terminology, datasets and limitations of these projections. Because each new phase of CMIP produces new projections under updated future climate scenarios and leads to a new round of downscaled climate projections, keeping up to date with modeling, scenarios and other changes between CMIP phases can be time consuming. This primer is intended to provide a broad overview of the two most recent phases of CMIP - the fifth phase of CMIP (CMIP5) and the sixth (CMIP6), as well as their relevant future climate scenarios and related downscaled climate model datasets. We also describe how MARISA products utilize these datasets and our planned changes to shift to the most recent CMIP6 projections.

Coupled Model Intercomparison Project 5 (CMIP5)

The fifth phase of CMIP, CMIP5, took place from roughly 2008 to 2013. Similar to other phases, it was managed by WCRP and included over 20 different global climate modeling groups and 50 different earth systems models.81 CMIP5 had a number of scientific goals, chief among them to evaluate and compare global climate models, as well as to produce a standardized set of projections of global climate change to 2100.82

To meet both scientific and practitioner needs, the CMIP5 effort produced a publicly-available set of global climate model outputs in a consistent format and under a consistent set of scenarios of future greenhouse gas emissions for 60 atmospheric variables including maximum daily temperature, humidity, windspeed and daily precipitation.83 These scenarios are termed Representative Concentration Pathways (RCPs) and are described in the next section.

These data served as the scientific basis for climate projections in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5), as well as in the Fourth National Climate Assessment (NCA4). 84,85 Outside of these national and global consensus efforts, CMIP5 data have been used at state to local levels for mitigation and adaptation planning throughout the United States. CMIP5 has now been updated to the sixth phase of CMIP, CMIP6, which is described below.

Coupled Model Intercomparison Project 6 (CMIP6)

In 2021, the sixth phase of CMIP, CMIP6, released a new suite of global climate model outputs from 100 distinct models and 49 different modeling groups that went through a development process similar to CMIP5. The number of modeling groups and models included in CMIP6 are double those from CMIP5, representing both the growing body of scientific work in global climate change as well as an overall expansion of the CMIP effort. CMIP6 also included a number of key technical updates. Additional or augmented model components and greater spatial and temporal resolutions enhanced the ability of models to capture key dynamics and smaller-scale processes or in key topographies, such as mountains.

The most notable technical difference between the suite of CMIP5 and CMIP6 models is their sensitivity to atmospheric greenhouse gas concentrations. Specifically, CMIP6 models have a higher climate sensitivity, meaning they project a greater change in global temperature for the same amount of atmospheric greenhouse gas concentrations.86 For many of the CMIP6 models, this is true for both the short-term climate response to emissions, and in the long-term.87 This is important as it helps characterize the scope and timing of potential warming to different trajectories of emissions. Because CMIP6 models also have a greater range in their climate sensitivity than those from CMIP5, scientific groups are able to better understand the role of climate sensitivity in producing changes in our global climate.

From a practitioner perspective, some literature has recommended excluding those CMIP6 models with extremely high climate sensitivities (termed "hot" models), but studies largely suggest changes in atmospheric variables have been relatively consistent across CMIP5 and CMIP6.88,89,90 Some studies suggest that CMIP6 has a potentially greater capacity to capture extremes, most notably for temperature, but a narrower range of projections (and thus smaller amount of uncertainty) for hydrologic indicators, such as precipitation variables. 91,92

Representative Concentration Pathways (RCPs)

Representative Concentration Pathways (RCPs) were developed as quantitative descriptions of future atmospheric greenhouse gas concentrations due to potential emissions trajectories and factors such as land use and population growth.93 RCPs were initially used as inputs to CMIP5 models in order to examine how future emissions would produce changes in our global climate.94 For CMIP5, four RCPs were developed, as shown in Table 7. RCPs are named for their level of radiative forcing, or the amount of additional energy entering Earth's atmosphere due to human activities. Of the RCPs, the most commonly used from CMIP5 are RCP4.5 and RCP8.5 which were generally considered as a pathway following current policies and emissions (RCP4.5) and a pathway with higher emissions (RCP8.5). CMIP6 introduced additional RCPs, which are detailed below with Shared Socioeconomic Pathways (SSPs).

Table 7. Main Characteristics of RCPs

Greenhouse Gas Emissions Agricultural Area Air Pollution
RCP2.683 Very low Medium for cropland and pasture Medium-low
RCP4.584 Medium-low Very low for cropland and pasture Medium
RCP685 Medium Medium for cropland and very low for pasture Medium
RCP8.586 High Medium for cropland and pasture Medium-high

Source: Table reproduced from VanVurren et al., 2012 https://link.springer.com/article/10.1007/s10584-011-0148-z#Tab2

Shared Socioeconomic Pathways (SSPs)

The other notable difference between CMIP5 and CMIP6 was how it conceptualized future climate scenarios. In CMIP5, global climate models were run under RCPs as their future scenarios. As described above, RCPs characterize different future pathways for atmospheric greenhouse gas concentrations. At the time scientists were developing RCPs, another group began modeling how socioeconomic factors may change in the future. This parallel effort was designed to complement the RCPs and when used together provide a more holistic set of trajectories of how human activities could influence global climate change. These socioeconomic pathways were termed Shared Socioeconomic Pathways (SSPs).

SSPs use narrative and quantitative descriptions of different potential pathways for how societies may evolve and change over time based on factors such as population growth, ecological and environmental conditions, inequality, and technological innovation.99 There are five different SSPs, each with a different story about the trajectory of the world100:

  • SSP1: Sustainability—Taking the Green Road. Low challenges to mitigation and adaptation.
  • SSP2: Middle of the Road. Medium challenges to mitigation and adaptation.
  • SSP3: Regional Rivalry—A Rocky Road.High challenges to mitigation and adaptation.
  • SSP4: Inequality—A Road Divided. Low challenges to mitigation, high challenges to adaptation.
  • SSP5: Fossil-fueled Development—Taking the Highway. High challenges to mitigation, low changes to adaptation.101

Because SSPs were fully published in 2017,102 they did not feature prominently in CMIP5 and instead form the basis of future climate scenarios for CMIP6. For CMIP6, the SSP futures were combined with an updated set of future atmospheric greenhouse gas concentrations (formerly RCPs) to provide the future climate scenarios, which carry the name SSPs, that served as inputs to CMIP6 models. Under CMIP6, RCPs were expanded from the four used in CMIP5 (2.6, 4.5, 6.0, 8.5) to also include futures with different levels of atmospheric greenhouse gas concentrations 1.9, 3.4 and 7.0. As an extension of RCPs, these are also named after their radiative forcing in 2100. Figure 7 illustrates how these were combined with the socioeconomic scenarios to form comprehensive SSPs. In data and literature, it is common to see SSPs described by their combination of SSP and their level of radiative forcing, such as SSP2-4.5, the combination of SSP2 and RCP4.5.

Figure 7. Overview of SSPs

How shared socioeconomic pathways (SSPs) are combined with future atmospheric greenhouse concentrations (formerly RCPs) to form comprehensive SSPs. For example, the combination of SSP2, Middle of the Road, and RCP4.5, is described as SSP2-4.5.

SOURCE: Reproduced from Chen et al., “Framing, Context, and Methods,” in Masson-Delmotte et al., eds., Climate Change 2021: The Physical Science Basis, p. 232, Cross-Chapter Box 1.4, CC BY-NC-ND 4.0.

To learn more about RCPs and SSPs, read the GLISA Practitioner’s Guide To Climate Model Scenarios.

Downscaled Climate Projections

The resolution of global climate models can be too large for some applications at regional and local scales. Fortunately, there are methods called downscaling that can take the global climate models and increase their spatial or temporal resolution. This increase in resolution does not necessarily make the downscaled models more accurate than the global models but develops datasets with resolutions more appropriate for local needs.

Because each phase of CMIP produces new climate projections, related downscaled climate model projections are developed in close succession. For CMIP5, the most commonly used downscaled products in the United States included Locally Constructed Analogs (LOCA), Multivariate Adaptive Constructed Analogs (MACA), and North America Coordinated Regional Downscaling Experiment (NA-CORDEX). Each dataset offers tradeoffs in terms of its downscaling approach, LOCA and MACA are statistically downscaled while NA-CORDEX is dynamically downscaled,103 as well as their temporal and spatial resolution. LOCA data is perhaps the most widely used and featured in work such as the National Climate Assessment. Technical details for these datasets are included in Table 8.

Table 8. Commonly Used CMIP5 Downscaled Climate Model Datasets

Dataset Number of Models RCPs Spatial Resolution Temporal Resolution
MACA 20 4.5, 8.5 2.5 miles Daily
LOCA 32 4.5, 8.5 3.7 miles Daily
NA-CORDEX 5 4.5, 8.5 15.5 to 31 miles Sub-daily to daily

SOURCE: Table reproduced from Miro et. al, 2021.99

NOTE: Spatial resolutions across all of the datasets are approximate and are based on conversions from degrees to miles at mid-latitudes.

For CMIP6, downscaled data products are still being produced, but a number have already been developed, including NASA Earth Exchange Global Daily Downscaled Projections

(NEX-GDDP-CMIP6), LOCA2 and Seasonal Trends and Analysis of Residuals, Empirical-Statistical Downscaling Model (STAR-ESDM). These are all statistically downscaled datasets. While use cases are still underway for these datasets, both LOCA2 and STAR-ESDM are featured in the Fifth National Climate Assessment (NCA5).

Table 9. Available CMIP6 Downscaled Climate Model Datasets

Dataset Number of Models SSPs Spatial Resolution Temporal Resolution
NEX-GDDP-CMIP6100 35 SSP1-2.6, SSP2-4.5, SSP3-7.0,
SSP5-8.5		 15.5 miles Daily
LOCA2101 27 SSP2-4.5, SSP3-7.0, SSP5-8.5 3.7 miles Daily
STAR-ESDM102 16 SSP2-4.5, SSP3-7.0, SSP5-8.5 15.5 miles Daily

All of our previous climate summaries have used the LOCA downscaled versions of the CMIP5 suite of climate models. Our future climate summaries will use the LOCA2 downscaled versions of the CMIP6 climate models. LOCA and LOCA2 both use statistical downscaling methods, but the historical training dataset and bias correction methods used in LOCA2 were designed to help do a better job at preserving any extremes in daily precipitation from global climate models.108 For a detailed comparison between the two, refer to the LOCA website: https://loca.ucsd.edu/loca-version-1-vs-loca-version-2/. We will be exploring the differences between the LOCA2 and LOCA climate projections in the next two climate summaries. For the upcoming Spring edition, we will take a look at the precipitation projections and for the Summer edition, we will look at temperature.

The Northeast Regional Climate Center recently held a webinar that included an initial examination of the differences between the LOCA and LOCA2 climate model projections for the Northeast region, which is available to view online.

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The Mid-Atlantic Regional Integrated Sciences and Assessments (MARISA) Seasonal Climate Impacts Summary and Outlook is a quarterly series produced by the MARISA program, a collaboration funded by NOAA through the RAND Corporation and researchers at Pennsylvania State University, Johns Hopkins University, Cornell University, the Virginia Institute of Marine Science, Morgan State University, and Carnegie Mellon University. This series is specifically designed to support policymakers, practitioners, residents, and community leaders in the Mid-Atlantic by serving as a data and information resource that is tailored to the region. It draws information from regional climate centers, news and weather information, and regional-specific climate data sets. Projections of weather and climate variability and change in the Mid-Atlantic region come from the best available scientific information. For any questions or comments, please contact Krista Romita Grocholski at Krista_Romita_Grocholski@rand.org.

This edition of the MARISA Seasonal Climate Impacts Summary and Outlook was authored by Krista Romita Grocholski (RAND Corporation), Michelle E. Miro (RAND Corporation), Lena Easton-Calabria (RAND Corporation), Samantha Borisoff (Cornell University), Jessica Spaccio (Cornell University), and Arthur T. DeGaetano (Cornell University).

Citation: Romita Grocholski, Krista, Michelle E. Miro, Lena Easton-Calabria, Samantha Borisoff, Jessica Spaccio, and Arthur T. DeGaetano, Mid-Atlantic Regional Climate Impacts Summary and Outlook: Winter 2023–2024. Santa Monica, CA: RAND Corporation, 2023.

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Footnotes

  1. https://www.axios.com/2024/01/16/snow-drought-ends-washington-new-york Return to text ⤴

  2. http://climod2.nrcc.cornell.edu/ Return to text ⤴

  3. http://climod2.nrcc.cornell.edu/ Return to text ⤴

  4. https://www.nrcc.cornell.edu/services/blog/2024/01/02/index.html Return to text ⤴

  5. https://www.nbcwashington.com/weather/snow-forecast-for-the-dc-area-on-monday-tuesday/3516114/ Return to text ⤴

  6. https://www.pennlive.com/news/2024/01/5-women-hit-killed-standing-on-i-81-after-crash-in-pa.html Return to text ⤴

  7. https://mesonet.agron.iastate.edu/wx/afos/p.php?pil=PNSLWX&e=202312111843 Return to text ⤴

  8. https://mesonet.agron.iastate.edu/wx/afos/p.php?pil=PNSLWX&e=202312111843 Return to text ⤴

  9. https://mesonet.agron.iastate.edu/wx/afos/p.php?pil=PNSAKQ&e=202312181507 Return to text ⤴

  10. https://www.weather.gov/bgm/pastFloodDecember182023 Return to text ⤴

  11. http://climod2.nrcc.cornell.edu/ Return to text ⤴

  12. http://climod2.nrcc.cornell.edu/ Return to text ⤴

  13. https://www.wnep.com/article/news/local/luzerne-county/heavy-rain-causes-problems-for-creeks-in-luzerne-county-solomon-creek-toby-back-mountain-wilkes-barre-flooding-flood/523-87ea0ee2-b242-4204-ad44-0553e478d1e8 Return to text ⤴

  14. https://local21news.com/news/local/drivers-rescued-from-the-roofs-of-their-cars-amid-flooding-in-lancaster-co Return to text ⤴

  15. https://www.nbcwashington.com/weather/weather-alert-dense-fog-soaking-rainstorm-to-arrive-sunday-in-dc-area/3496259/ Return to text ⤴

  16. https://www.delmarvanow.com/picture-gallery/news/local/maryland/2023/12/18/flooding-in-salisbury-crisfield-and-snow-hill-captured-in-photos/71962279007/ Return to text ⤴

  17. https://www.nbcwashington.com/weather/weather-alert-dense-fog-soaking-rainstorm-to-arrive-sunday-in-dc-area/3496259/ Return to text ⤴

  18. https://mesonet.agron.iastate.edu/wx/afos/p.php?pil=PNSAKQ&e=202312182039 Return to text ⤴

  19. https://www.abc27.com/local-news/man-dies-after-vehicle-submerges-in-lancaster-county-flood-waters/ Return to text ⤴

  20. https://mesonet.agron.iastate.edu/wx/afos/p.php?pil=PNSLWX&e=202312282116 Return to text ⤴

  21. https://www.wusa9.com/article/news/local/maryland/montgomery-county-flooding-strands-driver-brighton-dam-road/65-b5402585-107a-4de1-a3ec-9aeabf9a8268 Return to text ⤴

  22. https://mesonet.agron.iastate.edu/wx/afos/p.php?pil=PNSLWX&e=202401101306 Return to text ⤴

  23. https://mesonet.agron.iastate.edu/wx/afos/p.php?pil=PNSAKQ&e=202401101454 Return to text ⤴

  24. https://www.washingtonpost.com/weather/2024/01/10/dc-storm-flooding-record-rainfall/ Return to text ⤴

  25. https://mesonet.agron.iastate.edu/wx/afos/p.php?pil=LSRLWX&e=202401101943 Return to text ⤴

  26. https://mesonet.agron.iastate.edu/wx/afos/p.php?pil=LSRLWX&e=202401101943 Return to text ⤴

  27. https://mesonet.agron.iastate.edu/wx/afos/p.php?pil=PNSAKQ&e=202401100815 Return to text ⤴

  28. https://wtop.com/weather-news/2024/01/heavy-winds-rain-batter-dc-area-80-mph-wind-gusts-clocked-at-bay-bridge-prompting-4-hour-shutdown/ Return to text ⤴

  29. https://tidesandcurrents.noaa.gov/news_posts/article.html?post=2010 Return to text ⤴

  30. https://www.baltimoresun.com/2024/01/09/flooding-affects-baltimore-area-january-9-2024-photos/ Return to text ⤴

  31. https://patch.com/maryland/crofton/worst-flooding-20-years-annapolis-businesses-try-recover Return to text ⤴

  32. https://mesonet.agron.iastate.edu/wx/afos/p.php?pil=PNSBGM&e=202403011943 Return to text ⤴

  33. https://www.ncdc.noaa.gov/stormevents/listevents.jsp?eventType=%28C%29+Tornado&beginDate_mm=01&beginDate_dd=01&beginDate_yyyy=1950&endDate_mm=11&endDate_dd=30&endDate_yyyy=2023&county=BROOME%3A7&hailfilter=0.00&tornfilter=0&windfilter=000&sort=DT&submitbutton=Search&statefips=36%2CNEW+YORK Return to text ⤴

  34. https://mesonet.agron.iastate.edu/wx/afos/p.php?pil=PNSBGM&e=202403011943 Return to text ⤴

  35. https://droughtmonitor.unl.edu/data/png/20231205/20231205_va_trd.png Return to text ⤴

  36. https://droughtmonitor.unl.edu/data/png/20231205/20231205_md_trd.png Return to text ⤴

  37. https://droughtmonitor.unl.edu/data/png/20231205/20231205_huc02_trd.png Return to text ⤴

  38. https://droughtmonitor.unl.edu/data/png/20240102/20240102_huc02_trd.png Return to text ⤴

  39. https://droughtmonitor.unl.edu/data/png/20240102/20240102_va_trd.png Return to text ⤴

  40. https://www.potomacriver.org/wp-content/uploads/2023/12/WSO.December.23.pdf Return to text ⤴

  41. https://mailchi.mp/14023ea8366a/potomac-news-reservoir-december-14-2185784 Return to text ⤴

  42. https://www.ahs.dep.pa.gov/NewsRoomPublic/articleviewer.aspx?id=22382&typeid=1 Return to text ⤴

  43. https://www.ahs.dep.pa.gov/NewsRoomPublic/articleviewer.aspx?id=22382&typeid=1 Return to text ⤴

  44. https://www.nvdaily.com/nvdaily/strasburg-scales-back-drought-restrictions/article_7cdfd6f6-a84d-5437-8228-e9251e1a90c4.html#:~:text=The%20Strasburg%20town%20government%20downgraded,washing%20cars%20and%20home%20exteriors Return to text ⤴

  45. https://starexponent.com/news/local/preparing-for-continued-drought-town-to-connect-reserve-wells-to-water-system/article_8074fa48-907e-11ee-9368-472e0ac73b42.html Return to text ⤴

  46. https://droughtmonitor.unl.edu/data/png/20240130/20240130_huc02_cat.png Return to text ⤴

  47. https://droughtmonitor.unl.edu/DmData/DataTables.aspx Return to text ⤴

  48. https://droughtmonitor.unl.edu/data/png/20240130/20240130_huc02_cat.png Return to text ⤴

  49. https://www.yorkdispatch.com/story/news/local/2024/01/27/state-task-force-changes-york-countys-status-to-drought-watch/72378288007/ Return to text ⤴

  50. https://www.gazetteleader.com/fairfax/news/for-va-farmers-recent-wet-weather-is-a-mixed-blessing-8162325 Return to text ⤴

  51. https://www.whsv.com/2024/01/31/burnshire-hydroelectric-dam-feels-effects-shenandoah-rivers-high-water-flow-levels/ Return to text ⤴

  52. https://droughtmonitor.unl.edu/data/png/20240227/20240227_huc02_cat.png Return to text ⤴

  53. https://www.eveningsun.com/story/news/local/2024/02/07/with-newly-renovated-dam-filled-hanover-pa-ends-water-restrictions/72511351007/ Return to text ⤴

  54. https://www.loudounnow.com/news/towns/round-hill-lifts-mandatory-water-restrictions/article_3f5f8b70-c69a-11ee-a27f-73e62550930c.html Return to text ⤴

  55. https://www.washingtonpost.com/weather/2024/01/25/record-fog-united-states-explained/ Return to text ⤴

  56. https://www.washingtonpost.com/dc-md-va/2024/01/28/chesapeake-bay-bridge-crash-witness-fog/ Return to text ⤴

  57. Climate normals, as defined by the National Oceanic and Atmospheric Administration (NOAA), are "three-decade averages of climatological variables including temperature and precipitation." The latest climate normal released by NOAA is the 1991–2020 average. See https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals Return to text ⤴

  58. http://climod2.nrcc.cornell.edu/ Return to text ⤴

  59. http://climod2.nrcc.cornell.edu/ Return to text ⤴

  60. http://climod2.nrcc.cornell.edu/Return to text ⤴

  61. http://climod2.nrcc.cornell.edu/Return to text ⤴

  62. http://climod2.nrcc.cornell.edu/Return to text ⤴

  63. http://climod2.nrcc.cornell.edu/Return to text ⤴

  64. http://climod2.nrcc.cornell.edu/ Return to text ⤴

  65. http://climod2.nrcc.cornell.edu/Return to text ⤴

  66. http://climod2.nrcc.cornell.edu/ Return to text ⤴

  67. http://climod2.nrcc.cornell.edu/ Return to text ⤴

  68. http://climod2.nrcc.cornell.edu/ Return to text ⤴

  69. http://climod2.nrcc.cornell.edu/ Return to text ⤴

  70. https://www.cpc.ncep.noaa.gov/products/predictions/long_range/seasonal.php?lead=1 Return to text ⤴

  71. For more information on how NOAA defines at, above or below normal and determines percent chances, see: https://www.cpc.ncep.noaa.gov/products/predictions/long_range/seasonal_info.php Return to text ⤴

  72. https://www.cpc.ncep.noaa.gov/products/predictions/long_range/seasonal.php?lead=1 Return to text ⤴

  73. https://www.cpc.ncep.noaa.gov/products/expert_assessment/season_drought.png Return to text ⤴

  74. https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/ensodisc.shtml Return to text ⤴

  75. https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/ensodisc.shtml Return to text ⤴

  76. https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/ensodisc.html Return to text ⤴

  77. https://www.climatehubs.usda.gov/hubs/northwest/topic/basics-global-climate-models Return to text ⤴

  78. https://climate-scenarios.canada.ca/?page=cmip6-overview-notes Return to text ⤴

  79. https://glisa.umich.edu/wp-content/uploads/2021/03/A_Practitioners_Guide_to_Climate_Model_Scenarios.pdf Return to text ⤴

  80. https://climate-scenarios.canada.ca/?page=cmip6-overview-notes Return to text ⤴

  81. https://journals.ametsoc.org/view/journals/bams/93/4/bams-d-11-00094.1.xml Return to text ⤴

  82. http://cmip.llnl.gov/cmip5/index.html Return to text ⤴

  83. The full suite of CMIP5 models ranges from 0.125° x 0.125° to 5° x 5° depending on the model. Return to text ⤴

  84. https://www.ipcc.ch/report/ar5/syr/ Return to text ⤴

  85. https://nca2018.globalchange.gov/ Return to text ⤴

  86. https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019GL085782 Return to text ⤴

  87. https://www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained/ Return to text ⤴

  88. https://www.carbonbrief.org/guest-post-how-climate-scientists-should-handle-hot-models/ Return to text ⤴

  89. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7539979/ Return to text ⤴

  90. https://www.frontiersin.org/articles/10.3389/feart.2021.687976/full Return to text ⤴

  91. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JD033031 Return to text ⤴

  92. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022GL098364 Return to text ⤴

  93. https://link.springer.com/article/10.1007/s10584-011-0148-z Return to text ⤴

  94. https://glisa.umich.edu/wp-content/uploads/2021/03/A_Practitioners_Guide_to_Climate_Model_Scenarios.pdf Return to text ⤴

  95. The technical definition for this RCP is: "Peak in radiative forcing at ~3 W/m2 (~490 ppm CO2 eq) before 2100 and then decline (the selected pathway declines to 2.6 W/m2 by 2100)." VanVurren et al., 2012 https://link.springer.com/article/10.1007/s10584-011-0148-z#Tab2 Return to text ⤴

  96. The technical definition for this RCP is: "Stabilization without overshoot pathway to 4.5 W/m2 (~650 ppm CO2 eq) at stabilization after 2100." VanVurren et al., 2012 https://link.springer.com/article/10.1007/s10584-011-0148-z#Tab2 Return to text ⤴

  97. The technical definition for this RCP is: "Stabilization without overshoot pathway to 6 W/m2 (~850 ppm CO2 eq) at stabilization after 2100" VanVurren et al., 2012 https://link.springer.com/article/10.1007/s10584-011-0148-z#Tab2 Return to text ⤴

  98. The technical definition for this RCP is: "Rising radiative forcing pathway leading to 8.5 W/m2 (~1370 ppm CO2 eq) by 2100" VanVurren et al., 2012 https://link.springer.com/article/10.1007/s10584-011-0148-z#Tab2 Return to text ⤴

  99. https://glisa.umich.edu/wp-content/uploads/2021/03/A_Practitioners_Guide_to_Climate_Model_Scenarios.pdf Return to text ⤴

  100. https://glisa.umich.edu/wp-content/uploads/2021/03/A_Practitioners_Guide_to_Climate_Model_Scenarios.pdf; The link provides more detailed information about the SSPs. The original information comes from Riahi, K. et al., 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change. Volume 42. Pages 153-168, ISSN 0959-3780. Which may not be accessible to all readers. Return to text ⤴

  101. https://doi.org/10.1016/j.gloenvcha.2016.05.009 Return to text ⤴

  102. https://doi.org/10.1016/j.gloenvcha.2016.05.009 Return to text ⤴

  103. To learn more about the different ways that models can be downscaled, see https://climate.copernicus.eu/sites/default/files/2021-01/infosheet8.pdf. Return to text ⤴

  104. https://www.rand.org/pubs/tools/TLA1365-1.html Return to text ⤴

  105. https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf Return to text ⤴

  106. https://loca.ucsd.edu/loca-version-2-for-north-america-ca-jan-2023/ Return to text ⤴

  107. https://nca2023.globalchange.gov/chapter/appendix-3/#:~:text=Two%20datasets%20that%20employed%20different,ESDM)20%20%2C21%20(regional Return to text ⤴

  108. https://loca.ucsd.edu/loca-version-1-vs-loca-version-2/ Return to text ⤴

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