Stephen Po-Chedley

Precipitation extremes have increased globally in recent decades, consistent with a warming climate. We find that 2024 experienced a record-high frequency of heavy (≥99th percentile; F _P≥99 ) precipitation events globally based on MSWEP (Multi-Source Weighted-Ensemble Precipitation) observations from 1979 to 2025. This extends a long-term trend of ∼0.03%/decade in F _P≥99 , with similar but more uncertain increases for the 99.9th percentile. Unlike prior high F _P≥99 years, 2024’s record was more globally distributed and emerged from a distinct seasonal evolution. El Niño and rare concurrent multi-basin tropical ocean warmth drove elevated extremes early in the year, before lagged global tropospheric responses and long-term warming sustained and amplified extremes through boreal summer. Climate models indicate that years with 2024-level F _P≥99 typically require either stronger El Niño forcing or concurrent multi-basin sea surface temperature (SST) anomalies. Diagnostic projections within constrained ensemble members suggest that ∼40% of the 2024 anomaly aligns with global mean temperatures and ∼20% with El Niño variability, with some of the remainder linked to basin-scale SST structure.

Observed Arctic amplification (AA), defined as the ratio of Arctic‐mean to global‐mean surface air temperature (SAT) trends, peaks in April, a feature not captured by climate models. Here, we quantify the impact of internal variability on Arctic‐mean SAT trends in March, April, and May from 1980 to 2022 using multiple approaches, including machine learning algorithms—convolutional neural networks (CNNs) and partial least squares (ML‐PLS)—as well as dynamical adjustment and wind‐nudged experiments. Across all approaches, internal variability emerges as a substantial contributor to Arctic warming in these months, and is particularly prominent in April, when internal variability accounts for 40 ± 31% of the observed Arctic‐mean SAT trend, a result consistent across the CNN and wind‐nudged estimates. For each spring month, the linear methods estimate 4%–15% smaller contributions of internal variability than CNN estimates, suggesting that nonlinear approaches may more effectively isolate the internal variability component. Removing internal variability largely reconciles the springtime monthly AA discrepancy between climate models and observations. The strong spatial correlation between the observed‐minus‐multi‐model‐mean SAT trend pattern and that derived from wind‐nudged experiments further suggests that the simulated forced trend pattern is broadly realistic. Finally, the observed springtime monthly circulation–temperature configurations are rare in coupled climate models across all three spring months, suggesting that models have difficulty reproducing the magnitude of Arctic internal variability and/or the observed patterns are a rare manifestation.

This study presents wind and solar power generation estimates derived from the US Department of Energy’s Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) Regionally Refined Models (RRM) over California at 3.25 km and 800 m horizontal resolutions, using the Python wrapper for the System Advisor Model (PySAM). The resulting wind and solar generation estimates are compared to monthly capacity factors reported to the Energy Information Administration (EIA), High-Resolution Rapid Refresh (HRRR, 3 km resolution) forecast model, and E3SM North American regionally refined model (NARRM, 25 km resolution). We systematically assess the impacts of generation modeling assumptions, meteorological models, and horizontal resolution. Results show that resolution plays a dominant role for wind energy: increasing from 25 to 3.25 km brings qualitative and quantitative improvements, most notably by resolving the phase error in the seasonal cycle found in coarser simulations. However, further refinement to 800 m offers minimal gains. SCREAM performs better than HRRR for solar power generation in single- and dual-axis tracking systems, likely due to more accurate surface radiation. The sensitivity of PySAM to system configuration, particularly for axis-tracking modeling in photovoltaics, is also highlighted. Overall, SCREAM-RRM shows strong potential for high-resolution energy assessments, with future progress depending on more in situ observations and clearer quantification of uncertainties in generation modeling.

This study produces wind and solar power generation estimates derived from the US Department of Energy’s Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM) by leveraging regional mesh refinement over California (CARRM) simulations at 3.25 km and 800 m horizontal resolutions, using the Python wrapper of System Advisor Model (PySAM). The resulting wind and solar energy generation estimates are compared to monthly capacity factors from the Energy Information Administration (EIA), the High-Resolution Rapid Refresh (HRRR; 3 km resolution) forecast model, and the E3SM North American Regionally Refined Model (NARRM; 25 km resolution). We systematically assess the impacts of generation modeling assumptions, meteorological models, and horizontal resolution. Results show that resolution plays a dominant role for wind energy: increasing from 25 km to 3.25 km brings qualitative and quantitative improvements, most notably by resolving the phase error in the seasonal cycle found in coarser simulations. However, further refinement to 800 m offers minimal gains. SCREAM’s performance for solar generation surpasses HRRR, likely due to more accurate surface radiation. The sensitivity of PySAM to system configuration, particularly for axis-tracking modeling in photovoltaics, is also highlighted. Overall, SCREAM-RRM shows strong potential for high-resolution energy assessments, with future progress depending on more in situ observations and clearer quantification of generation modeling uncertainties.

This study produces wind and solar power generation estimates derived from the US Department of Energy’s Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM) by leveraging regional mesh refinement over California (CARRM) simulations at 3.25 km and 800 m horizontal resolutions, using the Python wrapper of System Advisor Model (PySAM). The resulting wind and solar energy generation estimates are compared to monthly capacity factors from the Energy Information Administration (EIA), the High-Resolution Rapid Refresh (HRRR; 3 km resolution) forecast model, and the E3SM North American Regionally Refined Model (NARRM; 25 km resolution). We systematically assess the impacts of generation modeling assumptions, meteorological models, and horizontal resolution. Results show that resolution plays a dominant role for wind energy: increasing from 25 km to 3.25 km brings qualitative and quantitative improvements, most notably by resolving the phase error in the seasonal cycle found in coarser simulations. However, further refinement to 800 m offers minimal gains. SCREAM’s performance for solar generation surpasses HRRR, likely due to more accurate surface radiation. The sensitivity of PySAM to system configuration, particularly for axis-tracking modeling in photovoltaics, is also highlighted. Overall, SCREAM-RRM shows strong potential for high-resolution energy assessments, with future progress depending on more in situ observations and clearer quantification of generation modeling uncertainties.

The pattern of Pacific sea-surface temperature (SST) change since 1980 has been highlighted as a key inconsistency between climate models and observations, with widespread impacts on the hydrological cycle, hurricane activity, sea level rise, and climate sensitivity. However, it is unknown whether this trend discrepancy results from discrepancies in the forced warming pattern simulated by climate models or discrepancies in simulated internal variability. Here, we use output of ForceSMIP, where statistical and machine learning models for distinguishing between forced response and internal variability within single realizations of the climate system were evaluated with climate model large ensembles and then applied to observations, to assess the forced and unforced contributions to Pacific SST trend discrepancies. We highlight a bias-variance tradeoff amongst the statistical and machine learning methods that show skill in forced response estimation, where methods that reduce the variance in estimated trends the most exhibit biases learned from the climate-model-based training data. Low-bias high-variance methods assess the trend discrepancy to be mostly forced, whereas low-variance high-bias methods assess the trend discrepancy to be mostly due to internal variability. The latter category of methods relies on training data from climate models with documented systematic biases, and we therefore suggest that more weight be put into the former category of methods, which would lead to the conclusion that the trend discrepancy is a discrepancy in the forced response. Our work illustrates the value of statistical attribution methods that are not reliant on climate models for interpreting trend discrepancies between climate models and observations.

This study produces wind and solar power generation estimates derived from the US Department of Energy’s Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM) by leveraging regional mesh refinement over California (CARRM) simulations at 3.25 km and 800 m horizontal resolutions, using the Python wrapper of System Advisor Model (PySAM). The resulting wind and solar energy generation estimates are compared to monthly capacity factors from the Energy Information Administration (EIA), the High-Resolution Rapid Refresh (HRRR; 3 km resolution) forecast model, and the E3SM North American Regionally Refined Model (NARRM; 25 km resolution). We systematically assess the impacts of generation modeling assumptions, meteorological models, and horizontal resolution. Results show that resolution plays a dominant role for wind energy: increasing from 25 km to 3.25 km brings qualitative and quantitative improvements, most notably by resolving the phase error in the seasonal cycle found in coarser simulations. However, further refinement to 800 m offers minimal gains. SCREAM’s performance for solar generation surpasses HRRR, likely due to more accurate surface radiation. The sensitivity of PySAM to system configuration, particularly for axis-tracking modeling in photovoltaics, is also highlighted. Overall, SCREAM-RRM shows strong potential for high-resolution energy assessments, with future progress depending on more in situ observations and clearer quantification of generation modeling uncertainties.

Earth's energy imbalance exhibits a robust positive trend over the past two decades, reaching a record value in 2023. Both the trend and year-to-year anomalies are primarily attributable to decreased reflection of sunlight by low-level clouds, which is pronounced over the Northern Hemisphere oceans. However, the causes of these low-cloud anomalies and their implications for future Earth system evolution are unknown, leading to speculation that the cloud feedback may be larger than expected. Here we quantify the meteorological factors that have driven interannual cloud-radiative anomalies, several of which aligned to cause the extreme global value observed in 2023. These meteorological variations are superposed on a background of decreasing sulfate aerosol concentrations, leading to a robust trend of reduced cloud reflection over the past 22 years. Constraints on cloud feedback and aerosol forcing implied by these observations support previous analyses and reaffirm an equilibrium climate sensitivity near 3°C with a likely (66% confidence) range of 2.7 to 4.1°C. Hence recent observations do not imply that a stronger-than-expected cloud feedback is emerging in nature or that future warming has been underestimated.