Abigail Jaye

Subseasonal‐to‐seasonal (S2S) forecasts are critically important for anticipating socioeconomic impacts but remain a significant scientific challenge. Reliable forecasts beyond a 2‐week timeframe require both accurate initial conditions and realistic representations of coupled Earth system processes. In particular, the Earth's land surface provides a key source of predictability for S2S predictions through land‐atmosphere interactions. Here, we present a 25‐year global 60‐km simulation using the Model for Prediction Across Scales (MPAS) coupled to the Noah‐MP land surface model, forced with ERA‐5 sea surface temperature and sea ice boundary conditions. This study evaluates the model's climatology, systematic biases, and ability to represent land‐atmosphere interactions, all of which are critical for future S2S applications. MPAS‐NoahMP captures well the annual surface temperature in the tropics and midlatitudes, while winter temperatures in North America and Asia are sensitive to sea ice thickness and snowpack thermal properties. The global precipitation pattern is also effectively reproduced by MPAS‐NoahMP, with slight dry biases in the tropics of the Eastern Pacific, Amazon, Congo Basin, and Maritime Continent, and wet biases in the tropical Indian ocean. The model accurately simulates spatial patterns of land‐atmosphere coupling indices and hotspots, including winter snow‐temperature coupling and summer soil moisture‐latent heat flux coupling. Furthermore, MPAS‐NoahMP reasonably represents the major modes of tropical variability, including the El Nino‐Southern Oscillation and the Madden‐Julian Oscillation. This study is the first to establish a long‐term MPAS‐NoahMP reference data set for understanding model biases and assessing land‐atmosphere interactions, providing a foundation for guiding future improvements to the coupled system for S2S applications.

The success of numerical weather prediction depends on accurate atmospheric initialization, but at subseasonal lead times, land and ocean initial states become increasingly important. Predictability on these timescales arises from slowly evolving land surface conditions such as soil moisture and snowpack, convectively coupled waves such as the Madden–Julian Oscillation and from oceanic variability including the El Niño–Southern Oscillation. While operational systems provide skillful subseasonal‐to‐seasonal forecasts, it remains uncertain whether this skill can be extended or if it reflects the intrinsic predictability limit. Using the Community Earth System Model in a perfect modeling framework, we estimate the theoretical limit of subseasonal‐to‐seasonal predictability from initialization. We find that over land, land initialization is the dominant source of predictability beyond week four, while ocean initialization plays a secondary role. Although the perfect modeling framework has limitations, our results suggest substantial potential to advance prediction through improved land initialization and representation of land–atmosphere coupling.

Forecast skill on subseasonal-to-seasonal (S2S) timescales varies strongly with the large-scale atmospheric state, creating intermittent “windows of opportunity’ ’ for skillful prediction. Here we evaluate state-dependent predictability of 2m temperature over North America using CESM2 and ECMWF subseasonal forecasts. Skill is quantified as a function of the Pacific-North American (PNA) pattern, the phase of the El Nino Southern Oscillation, the Madden-Julian Oscillation (MJO) and the North Atlantic Oscillation (NAO). Both models exhibit regionally significantly enhanced subseasonal skill during dynamically organized flow regimes like the PNA, El Niño or La Nina, and certain MJO phases, when tropical forcing projects onto an amplified winter jet and supports coherent Rossby wave propagation. The resulting predictability is modulated by the seasonality of the background flow. Our findings highlight the importance of the initial circulation state and demonstrate that regional S2S forecast skill may be higher than suggested by spatial averages.

The success of numerical weather prediction depends on accurate atmospheric initialization, but at subseasonal lead times, land and ocean initial states become increasingly important. Predictability on these timescales arises from slowly evolving land surface conditions such as soil moisture and snowpack, and from oceanic variability including the El Niño–Southern Oscillation and the Madden–Julian Oscillation. While operational systems provide skillful subseasonal-to-seasonal forecasts, it remains uncertain whether this skill can be extended or if it reflects the intrinsic predictability limit. Using the Community Earth System Model in a perfect modeling framework, we estimate the theoretical limit of subseasonal-to-seasonal predictability from initialization. We find that over land, land initialization is the dominant source of predictability beyond week four, while ocean initialization plays a secondary. Although the perfect modelling framework has limitations, our results suggest substantial potential to advance prediction through improved land initialization and representation of land–atmosphere coupling.

Subseasonal-to-seasonal (S2S) forecasts are critically important for anticipating socio-economic impacts but remain a significant scientific challenge. Reliable forecasts beyond a two-week timeframe require both accurate initial conditions and realistic representations of coupled Earth system processes. In particular, Earth’s land surface provides a key source of predictability for S2S predictions through land-atmosphere interactions. Here, we present a 25-year global 60-km simulation with the Model for Prediction Across Scales (MPAS) coupled with Noah-MP land surface model, with ERA-5 sea surface temperature and sea ice boundary conditions. This study evaluates the model’s climatology, systematic biases, and its ability to represent land-atmosphere interactions, all of which are necessary for future S2S applications. MPAS-NoahMP well captures annual surface temperature in the tropics and midlatitudes, while winter temperature in North America and Asia is sensitive to sea ice thickness and snowpack thermal properties. The global precipitation pattern is also well captured by MPAS-NoahMP, with slight dry biases in the tropics over the Eastern Pacific, Amazon, and Africa Congo, and wet biases in the Indian ocean and Maritime continent. The model accurately simulates spatial patterns of land-atmosphere coupling indices and hotspots, including winter snow-temperature coupling and summer soil moisture-latent heat flux coupling. Furthermore, MPAS-NoahMP reasonably captures the major modes of tropical variability, including the El Nino and Southern Oscillation and the Madden Julian Oscillation. This study is the first to establish a long-term MPAS-NoahMP reference dataset for understanding model biases and assessing land-atmosphere interactions, providing a foundation for extending the coupled MPAS-NoahMP system to future S2S applications.

Recently, Richter at al. 2024 investigated the sources of predictability from initializing the ocean, atmosphere and land components and verifying S2S predictions against observations. They find that ocean initialization adds little skill in weeks 4-6 and land initializations deteriorate skill in week 1-2. These results point to possible problems with spin-up and coupled model drift. Here we will revisit these results, but in a perfect modeling framework which eliminates model error. For the perfect model, we find that land initializations do contribute to skill, especially in the summer hemisphere. By studying the evolution of the lead-time dependent bias in the actual and perfect predictions, we attempt to disentangle initialization error from coupled model drift. Richter, J.H., Glanville, A.A., King, T. et al. Quantifying sources of subseasonal prediction skill in CESM2. npj Clim Atmos Sci 7, 59 (2024). https://doi.org/10.1038/s41612-024-00595-4

Holistic approaches are needed to investigate the capacity of current water resource operations and infrastructure to sustain water supply and critical ecosystem health under projected drought conditions. Drought vulnerability is complex, dynamic, and challenging to assess, requiring simultaneous consideration of changing water demand, use and management, hydrologic system response, and water quality. We are bringing together a community of scientists from the U.S. Geological Survey, National Center for Atmospheric Research, Department of Energy, and Cornell University to create an integrated human-hydro-terrestrial modeling framework, linking pre-existing models, that can explore and synthesize system response and vulnerability to drought in the Delaware River Basin (DRB). The DRB provides drinking water to over 15 million people in New York, New Jersey, Pennsylvania, and Delaware. Critical water management decisions within the system are coordinated through the Delaware River Basin Commission and must meet requirements set by prior litigation. New York City has rights to divert water from the upper basin for water supply but must manage reservoir releases to meet downstream flow and temperature targets. The Office of the Delaware River Master administers provisions of the Flexible Flow Management Program designed to manage reservoir releases to meet water supply demands, habitat, and specified downstream minimum flows to repel upstream movement of saltwater in the estuary that threatens Philadelphia public water supply and other infrastructure. The DRB weathered a major drought in the 1960s, but water resource managers do not know if current operations and water demands can be sustained during a future drought of comparable magnitude. The integrated human-hydro-terrestrial modeling framework will be used to identify water supply and ecosystem vulnerabilities to drought and will characterize system function and evolution during and after periods of drought stress. Models will be forced with consistent input data sets representing scenarios of past, present, and future conditions. The approaches used to unify and harmonize diverse data sets and open-source models will provide a roadmap for the broader community to replicate and extend to other water resource issues and regions.

Holistic approaches are needed to investigate the capacity of current water resource operations and infrastructure to sustain water supply and critical ecosystem health under projected drought conditions. Drought vulnerability is complex, dynamic, and challenging to assess, requiring simultaneous consideration of changing water demand, use and management, hydrologic system response, and water quality. We are bringing together a community of scientists from the U.S. Geological Survey, National Center for Atmospheric Research, Department of Energy, and Cornell University to create an integrated human-hydro-terrestrial modeling framework, linking pre-existing models, that can explore and synthesize system response and vulnerability to drought in the Delaware River Basin (DRB). The DRB provides drinking water to over 15 million people in New York, New Jersey, Pennsylvania, and Delaware. Critical water management decisions within the system are coordinated through the Delaware River Basin Commission and must meet requirements set by prior litigation. New York City has rights to divert water from the upper basin for water supply but must manage reservoir releases to meet downstream flow and temperature targets. The Office of the Delaware River Master administers provisions of the Flexible Flow Management Program designed to manage reservoir releases to meet water supply demands, habitat, and specified downstream minimum flows to repel upstream movement of saltwater in the estuary that threatens Philadelphia public water supply and other infrastructure. The DRB weathered a major drought in the 1960s, but water resource managers do not know if current operations and water demands can be sustained during a future drought of comparable magnitude. The integrated human-hydro-terrestrial modeling framework will be used to identify water supply and ecosystem vulnerabilities to drought and will characterize system function and evolution during and after periods of drought stress. Models will be forced with consistent input data sets representing scenarios of past, present, and future conditions. The approaches used to unify and harmonize diverse data sets and open-source models will provide a roadmap for the broader community to replicate and extend to other water resource issues and regions.

Five of California’s ten largest wildfires occurred in 2020, with the largest complex shattering the previous record by more than 100 %. The year follows a decade containing extraordinary fire activity. Trend investigations focused on changes in human activities and atmospheric thermodynamics, while the impacts of changing atmospheric dynamics are largely unknown. Here we identify extreme weather types (XWTs) associated with historically large daily burned areas in eight Californian regions. These XWTs characterize dominant fire weather regimes varying in fire behavior types (plume-driven vs. wind-driven fires) and seasonality. 2020’s exceptional fires partly occurred during previously unrecognized XWTs, whose characteristics and recurrence intervals were largely unknown. Most of the strongly large-scale forced XWTs such as Santa Ana and Diablo events increased in frequency during the 20th century particularly in the Sand Diego and Bay Area region. These changes are likely not anthropogenically caused and predominantly due to climate internal variability. However, raising greenhouse gas concentrations significantly decrease thermal low XWTs in southern and increase them in central California. These XWTs occur during the hottest time in the year and will alter fire risk in the summer season.

A framework to enable Earth system predictability research on the subseasonal timescale is developed with the Community Earth System Model, version 2 (CESM2) using two model configurations that differ in their atmospheric components. One configuration uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ~ 140 km in the vertical and it includes fully interactive tropospheric and stratospheric chemistry (CESM2(WACCM6)). Both configurations were used to carry out subseasonal reforecasts for the time period 1999 to 2020 following the Subseasonal Experiment’s (SubX) protocol. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-meter temperature, precipitation, the Madden-Julian Oscillation (MJO), and North Atlantic Oscillation (NAO) to the Community Earth System Model, version 1 with the Community Atmosphere Model, version 5 (CESM1(CAM5)) and to operational models. CESM2(CAM6) and CESM2(WACCM6) reforecast sets provide a comprehensive dataset for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region. We show that MLT variability can be predicted ~ 10 days in advance of sudden stratospheric warming events. Weekly real-time forecasts with CESM2(WACCM6) contribute to the multi-model mean ensemble forecast used to issue the NOAA weeks 3-4 outlooks. As a freely available community model, both CESM2 configurations can be used to carry out additional experiments to elucidate sources of subseasonal predictability.