An Online Ensemble Coupled Data Assimilation Capability for the Community Earth System Model: System Design and Evaluation

The Community Earth System Model (CESM) developed at the National Center of Atmospheric Research (NCAR) has been used worldwide for climate studies. This study extends the efforts of CESM development to include an online (i.e., in-core) ensemble coupled data assimilation system (CESM-ECDA) to enhance CESM’s capability for climate predictability studies and prediction applications. The CESM-ECDA system consists of an online atmospheric data assimilation (ADA) component implemented to both the finite-volume and spectral-element dynamical cores, and an online oceanic data assimilation (ODA) component. In ADA, surface pressures (Ps) are assimilated, while in ODA, gridded sea surface temperature (SST) and ocean temperature and salinity profiles at real Argo locations are assimilated. The system has been evaluated within a perfect twin experiment framework, showing significantly reduced errors of the model atmosphere and ocean states through “observation”-constraints by ADA and ODA. The weakly CDA in which both the online ADA and ODA are conducted during the coupled model integration shows smaller errors of air-sea fluxes than the single ADA and ODA, facilitating the future utilization of cross-covariance between the atmosphere and ocean at the air-sea interface. A three-year CDA reanalysis experiment is also implemented by assimilating Ps, SST and ocean temperature and salinity profiles from the real world spanning the period 1978 to 1980 using 12 ensemble members. Results show that Ps RMSE is smaller than 20CR and SST RMSE is better than ERA-20C and close to CFSR. The success of the online CESM-ECDA system is the first step to implement a high-resolution long-term climate reanalysis once the algorithm efficiency is much improved.

Elevation of the Gangdese Mountains and Their Impacts on Asian Climate During the Late Cretaceous—a Modeling Study

Uplift of the Gangdese Mountains is important to the evolution of Asian monsoons and the formation of Tibetan Plateau, but its paleoaltitude before the India-Asia collision (Late Cretaceous) is less constrained so far. In this study, we investigate whether the geological records, which are indicators of soil dryness, discovered in East Asia can provide such a constraint. Through climate modeling using the Community Earth System Model version 1.2.2, it is found that the extent of dry land in East Asia is sensitive to the altitude of the Gangdese Mountains. It expands eastwards and southwards with the rise of the mountain range. Comparison of the model results with all the available geological records in this region suggests that the Gangdese Mountains had attained a height of ∼2 km in the Late Cretaceous.

Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. This study aims to verify the global distribution of aerosol mixing state represented by modal models. To quantify the aerosol mixing state, we used the aerosol mixing state indices for submicron aerosol based on the mixing of optically absorbing and non-absorbing species (χo), the mixing of primary carbonaceous and non-primary carbonaceous species (χc), and the mixing of hygroscopic and non-hygroscopic species (χh). To achieve a spatiotemporal comparison, we calculated the mixing state indices using output from the Community Earth System Model with the four-mode version of the Modal Aerosol Module (MAM4) and compared the results with the mixing state indices from a benchmark machine-learned model trained on high-detail particle-resolved simulations from the particle-resolved stochastic aerosol model PartMC-MOSAIC. The two methods yielded very different spatial patterns of the mixing state indices. In some regions, the yearly averaged χ value computed by the MAM4 model differed by up to 70 percentage points from the benchmark values. These errors tended to be zonally structured, with the MAM4 model predicting a more internally mixed aerosol at low latitudes and a more externally mixed aerosol at high latitudes compared to the benchmark. Our study quantifies potential model bias in simulating mixing state in different regions and provides insights into potential improvements to model process representation for a more realistic simulation of aerosols towards better quantification of radiative forcing and aerosol–cloud interactions.

Famines and likelihood of consecutive megadroughts in India

Consecutive failures in the summer monsoon rainfall led to widespread and severe droughts with profound implications for agricultural activities in India. However, the likelihood of successive megadroughts in India’s past and future climate remain poorly understood. Using Palmer Drought Severity Index (PDSI) from the Monsoon Asia Drought Atlas (MADA), we show that the major famines that affected millions of people during 1200–2018 were linked with summer monsoon droughts. Four megadroughts covering more than 40% of the country occurred for two consecutive summer monsoon seasons during 1200–2018. The most recent and severe megadrought occurred in 2002–2003. Simulations from the Community Earth System Model (CESM) for the last millennium (850–2005) ensemble (LME) show that the likelihood of two and three-year consecutive megadroughts during the summer monsoon is about 0.7 and 0.3 events per 100 years, respectively. Large ensemble simulations from CESM (CESM-LE) show a decline in the frequency of megadroughts in the future. Summer monsoon megadroughts are strongly associated with the warm sea surface temperature (SST) anomalies over the Pacific Ocean in the past and future climate. Substantial warming under the projected future climate can cause megadroughts under near-normal precipitation during the summer monsoon season. Despite the projected decline in the likelihood of the summer monsoon megadroughts under the warming climate, megadroughts in the future can have considerable implications for India’s food production and water availability.

Energetic constraints on the time-dependent response of the ITCZ to volcanic eruptions

Energetic constraints on the time-dependent response of the intertropical convergence zone (ITCZ) to volcanic eruptions are analyzed using the Community Earth System Model Last Millennium Ensemble project. The energetic constraints are found to vary during the first few years, governed by conjoined variations of the energy budgets of the stratosphere, troposphere, and oceans. Specifically, following eruptions, sulfate aerosols heat the stratosphere by long-wave absorption and cool the surface by shortwave reflection, leading to contrasting energy transport anomalies in the stratosphere and troposphere, which are of comparable strength during the first year. Similar contrasting responses are also seen by the mean and eddy components of atmospheric energy transport (AET). Consequently, ocean energy transport (OET) dominates the anomalous total interhemispheric energy transport during the first year. However, wind-driven OET, generally assumed to constrain shifts of the ITCZ, has a negligible role in the transient ocean response. Consistent with theory, anomalous cross-equatorial tropospheric energy transport, dominated by the anomalous Hadley circulation, is strongly negatively correlated with ITCZ shifts. However, due to the strong anomalous stratospheric energy fluxes, the commonly used energy flux equator (derived from net AET) is a poor predictor of transient ITCZ shifts following eruptions. El Niño-like conditions typically appear during the second year after eruptions, and La Niña-like conditions after the third year. These variations modulate ITCZ shifts in a complex manner, via changes in surface conditions and in associated energy transport variations in the atmosphere and oceans.

Influence of atmospheric in-cloud aqueous-phase chemistry on the global simulation of SO₂ in CESM2

Sulfur dioxide (SO2) is a major atmospheric pollutant and precursor of sulfate aerosols, which influences air quality, cloud microphysics, and climate. Therefore, better understanding the conversion of SO2 to sulfate is essential to simulate and predict sulfur compounds more accurately. This study evaluates the effects of in-cloud aqueous-phase chemistry on SO2 oxidation in the Community Earth System Model version 2 (CESM2). We replaced the default parameterized SO2 aqueous-phase reactions with detailed HOx, Fe, N, and carbonate chemistry in cloud droplets and performed a global simulation for 2014–2015. Compared with the observations, the results incorporating detailed cloud aqueous-phase chemistry greatly reduced SO2 overestimation. This overestimation was reduced by 0.1–10 ppbv (parts per billion by volume) in most of Europe, North America, and Asia and more than 10 ppbv in parts of China. The biases in annual simulated SO2 mixing ratios decreased by 46 %, 41 %, and 22 % in Europe, the USA, and China, respectively. Fe chemistry and HOx chemistry contributed more to SO2 oxidation than N chemistry. Higher concentrations of soluble Fe and higher pH values could further enhance the oxidation capacity. This study emphasizes the importance of detailed in-cloud aqueous-phase chemistry for the oxidation of SO2. These mechanisms can improve SO2 simulation in CESM2 and deepen understanding of SO2 oxidation and sulfate formation.

Contrasting response of hydrological cycle over land and ocean to a changing CO2 pathway

The hydrological cycle has a significant impact on human activities and ecosystems, so understanding its mechanisms with respect to a changing climate is essential. In particular, a more detailed understanding of hydrological cycle response to transient climate change is required for successful adaptation and mitigation policies. In this study, we exploit large ensemble model experiments using the Community Earth System Model version 1.2.2 (CESM1) in which CO2 concentrations increase steadily and then decrease along the same path. Our results show that precipitation changes in the CO2 increasing and decreasing phases are nearly symmetrical over land but asymmetric over oceans. After CO2 concentrations peak, the ocean continues to uptake heat from the atmosphere, which is a key process leading the hydrological cycle’s contrasting response over land and ocean. The symmetrical hydrological cycle response over land involves a complex interplay between rapid responses to CO2 and slower responses to ensuing warming. Therefore, the surface energy constraints lead to the contrasting hydrological response over land and ocean to CO2 forcing that needs to be verified and considered in climate change mitigation and adaption actions.

The relationship between PM2.5 and anti-cyclone wave activity during summer over the United States

To better understand the role of atmospheric dynamics in modulating surface concentrations of fine particulate matter (PM2.5), we relate the anti-cyclone wave activity (AWA) metric and PM2.5 data from the Interagency Monitoring of Protected Visual Environment (IMPROVE) data for the period of 1988–2014 over the US. The observational results are compared with hindcast simulations over the past two decades using the National Center for Atmospheric Research-Community Earth System Model (NCAR CESM). We find that PM2.5 is positively correlated (up to R = 0.65) with AWA changes close to the observing sites using regression analysis. The composite AWA for high aerosol days (all daily PM2.5 above the 90th percentile) shows a similarly strong correlation between PM2.5 and AWA. The most prominent correlation occurs in the Midwestern US. Furthermore, the higher quantiles of PM2.5 levels are more sensitive to the changes in AWA. For example, we find the averaged sensitivity of the 90th percentile PM2.5 to changes in AWA is approximately three times as strong as the sensitivity of 10th percentile PM2.5 at one site (Arendtsville, Pennsylvania; 39.92° N, 77.31° W). The higher values of the 90th percentile compared to the 50th percentile in quantile regression slopes are most prominent over the northeastern US. In addition, future changes in US PM2.5 based only on changes in climate are estimated to increase PM2.5 concentrations due to increased AWA in summer over areas where PM2.5 variations are dominated by meteorological changes, especially over the western US. Changes between current and future climates in AWA can explain up to 75 % of PM2.5 variability using a linear regression model. Our analysis indicates that higher PM2.5 concentrations occur when a positive AWA anomaly is prominent, which could be critical for understanding how pollutants respond to changing atmospheric circulation, as well as developing robust pollution projections.