Winter storms and the Spring Transition over the western U.S.: Quantifying discrepancies between coarse and high-resolution simulations and observations

This project addressed the ability of the Community Climate System Model (CCSM3 and CCSM4), the Community Earth System Model (CESM), and other models to simulate the processes involved in controlling winter storms affecting the U.S. West Coast as well as other precipitation processes in the climate system

The role of surface roughness, albedo, and Bowen ratio on ecosystem energy balance in the Eastern United States

Land cover and land use influence surface climate through differences in biophysical surface properties, including partitioning of sensible and latent heat (e.g., Bowen ratio), surface roughness, and albedo. Clusters of closely spaced eddy covariance towers (e.g., \u3c10 \u3ekm) over a variety of land cover and land use types provide a unique opportunity to study the local effects of land cover and land use on surface temperature. We assess contributions albedo, energy redistribution due to differences in surface roughness and energy redistribution due to differences in the Bowen ratio using two eddy covariance tower clusters and the coupled (land-atmosphere) Variable-Resolution Community Earth System Model. Results suggest that surface roughness is the dominant biophysical factor contributing to differences in surface temperature between forested and deforested lands. Surface temperature of open land is cooler (−4.8 °C to −0.05 °C) than forest at night and warmer (+0.16 °C to +8.2 °C) during the day at northern and southern tower clusters throughout the year, consistent with modeled calculations. At annual timescales, the biophysical contributions of albedo and Bowen ratio have a negligible impact on surface temperature, however the higher albedo of snow-covered open land compared to forest leads to cooler winter surface temperatures over open lands (−0.4 °C to −0.8 °C). In both the models and observation, the difference in mid-day surface temperature calculated from the sum of the individual biophysical factors is greater than the difference in surface temperature calculated from radiative temperature and potential temperature. Differences in measured and modeled air temperature at the blending height, assumptions about independence of biophysical factors, and model biases in surface energy fluxes may contribute to daytime biases

Global and regional importance of the direct dust-climate feedback.

Feedbacks between the global dust cycle and the climate system might have amplified past climate changes. Yet, it remains unclear what role the dust-climate feedback will play in future anthropogenic climate change. Here, we estimate the direct dust-climate feedback, arising from changes in the dust direct radiative effect (DRE), using a simple theoretical framework that combines constraints on the dust DRE with a series of climate model results. We find that the direct dust-climate feedback is likely in the range of -0.04 to +0.02 Wm -2 K-1, such that it could account for a substantial fraction of the total aerosol feedbacks in the climate system. On a regional scale, the direct dust-climate feedback is enhanced by approximately an order of magnitude close to major source regions. This suggests that it could play an important role in shaping the future climates of Northern Africa, the Sahel, the Mediterranean region, the Middle East, and Central Asia

Integrating Empirical Orthogonal Functions (EOFs) into Machine Learning Model

The representation of soil moisture in Earth System Models, like the Community Earth System Model (CESM), is an essential facet in modeling the response of the Earth System to climate change. Since their inception, land models have grown to represent critical processes like carbon cycling, ecosystem dynamics, terrestrial hydrology, and agriculture. They serve as a lower boundary condition for atmospheric general circulation models. With increasing process representation, they are computationally expensive. Hydrologists and modelers use several parameterization schemes to describe the water and energy balance. However, this is regarded as computationally expensive. Alternative tools called emulators (e.g., machine learning and artificial intelligence) incorporated with the empirical orthogonal function analysis can represent soil moisture

Influence of Sea-Ice Anomalies on Antarctic Precipitation Using Source Attribution in the Community Earth System Model

We conduct sensitivity experiments using a general circulation model that has an explicit water source tagging capability forced by prescribed composites of pre-industrial sea-ice concentrations (SICs) and corresponding sea surface temperatures (SSTs) to understand the impact of sea-ice anomalies on regional evaporation, moisture transport and sourcereceptor relationships for Antarctic precipitation in the absence of anthropogenic forcing. Surface sensible heat fluxes, evaporation and column-integrated water vapor are larger over Southern Ocean (SO) areas with lower SICs. Changes in Antarctic precipitation and its source attribution with SICs have a strong spatial variability. Among the tagged source regions, the Southern Ocean (south of 50 S) contributes the most (40 %) to the Antarctic total precipitation, followed by more northerly ocean basins, most notably the South Pacific Ocean (27%), southern Indian Ocean (16 %) and South Atlantic Ocean (11 %). Comparing two experiments prescribed with high and low pre-industrial SICs, respectively, the annual mean Antarctic precipitation is about 150 Gt yr1 (or 6 %) more in the lower SIC case than in the higher SIC case. This difference is larger than the model-simulated interannual variability in Antarctic precipitation (99 Gt yr1). The contrast in contribution from the Southern Ocean, 102 Gt yr1, is even more significant compared to the interannual variability of 35 Gt yr1 in Antarctic precipitation that originates from the Southern Ocean. The horizontal transport pathways from individual vapor source regions to Antarctica are largely determined by large-scale atmospheric circulation patterns. Vapor from lower-latitude source regions takes elevated pathways to Antarctica. In contrast, vapor from the Southern Ocean moves southward within the lower troposphere to the Antarctic continent along moist isentropes that are largely shaped by local ambient conditions and coastal topography. This study also highlights the importance of atmospheric dynamics in affecting the thermodynamic impact of sea-ice anomalies associated with natural variability on Antarctic precipitation. Our analyses of the seasonal contrast in changes of basin-scale evaporation, moisture flux and precipitation suggest that the impact of SIC anomalies on regional Antarctic precipitation depends on dynamic changes that arise from SICSST perturbations along with internal variability. The latter appears to have a more significant effect on the moisture transport in austral winter than in summer

Sea ice trends in climate models only accurate in runs with biased global warming

Observations indicate that the Arctic sea ice cover is rapidly retreating while the Antarctic sea ice cover is steadily expanding. State-of-the-art climate models, by contrast, typically simulate a moderate decrease in both the Arctic and Antarctic sea ice covers. However, in each hemisphere there is a small subset of model simulations that have sea ice trends similar to the observations. Based on this, a number of recent studies have suggested that the models are consistent with the observations in each hemisphere when simulated internal climate variability is taken into account. Here we examine sea ice changes during 1979-2013 in simulations from the most recent Coupled Model Intercomparison Project (CMIP5) as well as the Community Earth System Model Large Ensemble (CESM-LE), drawing on previous work that found a close relationship in climate models between global-mean surface temperature and sea ice extent. We find that all of the simulations with 1979-2013 Arctic sea ice retreat as fast as observed have considerably more global warming than observations during this time period. Using two separate methods to estimate the sea ice retreat that would occur under the observed level of global warming in each simulation in both ensembles, we find that simulated Arctic sea ice retreat as fast as observed would occur less than 1% of the time. This implies that the models are not consistent with the observations. In the Antarctic, we find that simulated sea ice expansion as fast as observed typically corresponds with too little global warming, although these results are more equivocal. We show that because of this, the simulations do not capture the observed asymmetry between Arctic and Antarctic sea ice trends. This suggests that the models may be getting the right sea ice trends for the wrong reasons in both polar regions

Climate Change from 1850 to 2005 Simulated in CESM1(WACCM)

The NCAR Community Earth System Model (CESM) now includes an atmospheric component that extends in altitude to the lower thermosphere. This atmospheric model, known as the Whole Atmosphere Community Climate Model (WACCM), includes fully interactive chemistry, allowing, for example, a self-consistent representation of the development and recovery of the stratospheric ozone hole and its effect on the troposphere. This paper focuses on analysis of an ensemble of transient simulations using CESM1(WACCM), covering the period from the preindustrial era to present day, conducted as part of phase 5 of the Coupled Model Intercomparison Project. Variability in the stratosphere, such as that associated with stratospheric sudden warmings and the development of the ozone hole, is in good agreement with observations. The signals of these phenomena propagate into the troposphere, influencing near-surface winds, precipitation rates, and the extent of sea ice. In comparison of tropospheric climate change predictions with those from a version of CESM that does not fully resolve the stratosphere, the global-mean temperature trends are indistinguishable. However, systematic differences do exist in other climate variables, particularly in the extratropics. The magnitude of the difference can be as large as the climate change response itself. This indicates that the representation of stratosphere–troposphere coupling could be a major source of uncertainty in climate change projections in CESM