Impact of urban expansion on continental- to regional-scale warming

Although the influence of land use/land cover change on climate has become increasingly apparent, cities and other built-up areas are usually ignored when estimating large-scale historical climate change or for future projections since cities cover a small fraction of the terrestrial land surface1,2. As such, ground-based observations of urban near-surface meteorology are rare and most earth system models do not represent historical or future urban land cover3–7. Here, by combining global satellite observations of land surface temperature with historical estimates of built-up area, we demonstrate that the urban temperature signal on continental- to regional-scale warming has become non-negligible, especially for rapidly urbanizing regions in Asia. Consequently, expected urban expansion over the next century suggest further increased urban influence on surface climate under all future climate scenarios. Based on these results, we argue that, in line with other forms of land use/land cover change, urbanization should be explicitly included in future climate change assessments. This would require extensive model development to incorporate urban extent and biophysics in current-generation earth system models to quantify potential urban feedbacks on the climate system at multiple scales.

Climate change penalty and benefit on surface ozone: A global perspective based on CMIP6 earth system models

This work presents an analysis of the effect of climate change on surface ozone discussing the related penalties and benefits around the globe from the global modeling perspective based on simulations with five CMIP6 (Coupled Model Intercomparison Project Phase 6) Earth System Models. As part of AerChemMIP (Aerosol Chemistry Model Intercomparison Project) all models conducted simulation experiments considering future climate (ssp370SST) and present-day climate (ssp370pdSST) under the same future emissions trajectory (SSP3-7.0). A multi-model global average climate change benefit on surface ozone of -0.96±0.07 ppbv oC-1 is calculated which is mainly linked to the dominating role of enhanced ozone destruction with higher water vapour abudances under a warmer climate. Over regions remote from pollution sources, there is a robust decline in mean surface ozone concentration on an annual basis as well as for boreal winter and summer varying spatially from -0.2 to -2 ppbv oC-1, with strongest decline over tropical oceanic regions. The implication is that over regions remote from pollution sources (except over the Arctic) there is a consistent climate change benefit for baseline ozone due to global warming. However, ozone increases over regions close to anthropogenic pollution sources or close to enhanced natural Biogenic Volatile Organic Compounds (BVOC) emission sources with a rate ranging regionally from 0.2 to 2 ppbv oC-1, implying a regional surface ozone penalty due to global warming. Overall, the future climate change enhances the efficiency of precursor emissions to generate surface ozone in polluted regions and thus the magnitude of this effect depends on the regional emission changes considered in this study within the SSP3_7.0 scenario. The comparison of the climate change impact effect on surface ozone versus the combined effect of climate and emission changes indicates the dominant role of precursor emission changes in projecting surface ozone concentrations under future climate change scenarios.

Pattern-based downscaling of snowpack variability in the western United States

The decline in snowpack across the western United States is one of the most pressing threats posed by climate change to regional economies and livelihoods. Earth system models are important tools for exploring past and future snowpack variability, yet their coarse spatial resolutions distort local topography and bias spatial patterns of accumulation and ablation. Here, we explore pattern-based statistical downscaling for spatially-continuous interannual snowpack estimates. We find that a few leading patterns capture the majority of snowpack variability across the western US in observations, reanalyses, and free-running simulations. Pattern-based downscaling methods yield accurate, high resolution maps that correct mean and variance biases in domain-wide simulated snowpack. Methods that use large-scale patterns as both predictors and predictands perform better than those that do not and all are superior to an interpolation-based “delta change” approach. These findings suggest that pattern-based methods are appropriate for downscaling interannual snowpack variability and that using physically meaningful large-scale patterns is more important than the details of any particular downscaling method.

The role of anthropogenic aerosols in the anomalous cooling from 1960 to 1990 in the CMIP6 Earth system models

The Earth system models (ESMs) that participated in the sixth Coupled Model Intercomparison Project (CMIP6) tend to simulate excessive cooling in surface air temperature (TAS) between 1960 and 1990. The anomalous cooling is pronounced over the Northern Hemisphere (NH) midlatitudes, coinciding with the rapid growth of anthropogenic sulfur dioxide (SO2) emissions, the primary precursor of atmospheric sulfate aerosols. Structural uncertainties between ESMs have a larger impact on the anomalous cooling than internal variability. Historical simulations with and without anthropogenic aerosol emissions indicate that the anomalous cooling in the ESMs is attributed to the higher aerosol burden in these models. The aerosol forcing sensitivity, estimated as the outgoing shortwave radiation (OSR) response to aerosol concentration changes, cannot well explain the diversity of pothole cooling (PHC) biases in the ESMs. The relative contributions to aerosol forcing sensitivity from aerosol–radiation interactions (ARIs) and aerosol–cloud interactions (ACIs) can be estimated from CMIP6 simulations. We show that even when the aerosol forcing sensitivity is similar between ESMs, the relative contributions of ARI and ACI may be substantially different. The ACI accounts for between 64 % and 87 % of the aerosol forcing sensitivity in the models and is the main source of the aerosol forcing sensitivity differences between the ESMs. The ACI can be further decomposed into a cloud-amount term (which depends linearly on cloud fraction) and a cloud-albedo term (which is independent of cloud fraction, to the first order), with the cloud-amount term accounting for most of the inter-model differences.

ChAP 1.0: a stationary tropospheric sulfur cycle for Earth system models of intermediate complexity

A stationary, computationally efficient scheme ChAP 1.0 (Chemical and Aerosol Processes, version 1.0) for the sulfur cycle in the troposphere is developed. This scheme is designed for Earth system models of intermediate complexity (EMICs). The scheme accounts for sulfur dioxide emissions into the atmosphere, its deposition to the surface, oxidation to sulfates, and dry and wet deposition of sulfates on the surface. The calculations with the scheme are forced by anthropogenic emissions of sulfur dioxide into the atmosphere for 1850–2000 adopted from the CMIP5 dataset and by the ERA-Interim meteorology assuming that natural sources of sulfur into the atmosphere remain unchanged during this period. The ChAP output is compared to changes of the tropospheric sulfur cycle simulations with the CMIP5 data, with the IPCC TAR ensemble, and with the ACCMIP phase II simulations. In addition, in regions of strong anthropogenic sulfur pollution, ChAP results are compared to other data, such as the CAMS reanalysis, EMEP MSC-W, and individual model simulations. Our model reasonably reproduces characteristics of the tropospheric sulfur cycle known from these information sources. In our scheme, about half of the emitted sulfur dioxide is deposited to the surface, and the rest is oxidised into sulfates. In turn, sulfates are mostly removed from the atmosphere by wet deposition. The lifetimes of the sulfur dioxide and sulfates in the atmosphere are close to 1 and 5 d, respectively. The limitations of the scheme are acknowledged, and the prospects for future development are figured out. Despite its simplicity, ChAP may be successfully used to simulate anthropogenic sulfur pollution in the atmosphere at coarse spatial scales and timescales.