Sensitivity of terrestrial precipitation trends to the structural evolution of sea surface temperatures

Pronounced intermodel differences in the projected response of land surface precipitation (LSP) to future anthropogenic forcing remain in the Coupled Model Intercomparison Project Phase 5 model integrations. A large fraction of the intermodel spread in projected LSP trends is demonstrated here to be associated with systematic differences in simulated sea surface temperature (SST) trends, especially the representation of changes in (i) the interhemispheric SST gradient and (ii) the tropical Pacific SSTs. By contrast, intermodel differences in global mean SST, representative of differing global climate sensitivities, exert limited systematic influence on LSP patterns. These results highlight the importance to regional terrestrial precipitation changes of properly simulating the spatial distribution of large-scale, remote changes as reflected in the SST response to increasing greenhouse gases. Moreover, they provide guidance regarding which region-specific precipitation projections may be potentially better constrained for use in climate change impact assessments

North American Climate in CMIP5 Experiments: Part III: Assessment of Twenty-First-Century Projections

In part III of a three-part study on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) models, the authors examine projections of twenty-first-century climate in the representative concentration pathway 8.5 (RCP8.5) emission experiments. This paper summarizes and synthesizes results from several coordinated studies by the authors. Aspects of North American climate change that are examined include changes in continental-scale temperature and the hydrologic cycle, extremes events, and storm tracks, as well as regional manifestations of these climate variables. The authors also examine changes in the eastern North Pacific and North Atlantic tropical cyclone activity and North American intraseasonal to decadal variability, including changes in teleconnections to other regions of the globe. Projected changes are generally consistent with those previously published for CMIP3, although CMIP5 model projections differ importantly from those of CMIP3 in some aspects, including CMIP5 model agreement on increased central California precipitation. The paper also highlights uncertainties and limitations based on current results as priorities for further research. Although many projected changes in North American climate are consistent across CMIP5 models, substantial intermodel disagreement exists in other aspects. Areas of disagreement include projections of changes in snow water equivalent on a regional basis, summer Arctic sea ice extent, the magnitude and sign of regional precipitation changes, extreme heat events across the northern United States, and Atlantic and east Pacific tropical cyclone activity

Quantifying uncertainty in precipitation climatology, twenty-first century change, and teleconnections in global climate models

The ability of global climate models (GCMs) to simulate climatological precipitation and other features of the hydrological cycle accurately is acceptable by some metrics, especially at large scales. Regionally, however, there can be substantial discrepancy in a multi-model ensemble, both in the annual or seasonal historical precipitation climatology as well as in end-of-century changes. Characterizing this intermodel spread and identifying leading uncertainty patterns and underlying physical pathways is important in constraining climatological biases and projections of future change. This dissertation looks at three aspects of precipitation uncertainty in ensembles.First, El Nino-Southern Oscillation (ENSO) teleconnections are analyzed in an atmosphere-only ensemble to gauge the ability of atmospheric components of GCMs to reproduce ENSO precipitation teleconnections. This serves as a test for how well models simulate the atmospheric response to sea surface temperature forcing in the immediate ENSO vicinity, as well as how accurately they reproduce the large-scale tropical-to-midlatitude dynamics leading to teleconnected precipitation. While individual models have difficulty in simulating the exact spatial pattern of teleconnections, they demonstrate skill in regional amplitude measures and sign agreement of the precipitation teleconnections at the grid point level, which lends value to the use of such measures in global warming projections.Next, objective spatial analysis techniques are applied to a fully-coupled GCM ensemble in order to visualize patterns of uncertainty in end-of-century precipitation changes and in the historical climatology. Global patterns are considered first, with the tropics exerting a clear dominance in intermodel spread, mainly within zones of deep convection or along convective margins. Regional domains are considered second, with a focus on the wintertime midlatitude Pacific storm track. A key region of end-of-century precipitation change uncertainty is identified at the terminus of the storm track, and large-scale circulation processes related to model differences in upper-level jet increases are found to play a role. These results help pinpoint a source of intermodel spread in projected precipitation changes along the North American west coast, especially for the Southern California region.Last, an existing perturbed physics ensemble is examined in order to understand the parameter sensitivity of climatological precipitation and other fields. This ensemble consists of integrations in which four parameters in the deep convection scheme were systematically varied. Models of parameter dependence are constructed for precipitation, and this process--termed metamodeling--is a computationally cheap alternative to brute-force sampling of parameter space in the GCM. A quadratic metamodel performs generally well but fails to capture sensitive regions of high nonlinearity for certain parameter ranges. A second metamodel is constructed by combining an approach from the engineering literature with the spatial uncertainty patterns used above, and it proves adept at capturing sensitive regions where its quadratic counterpart fails. Finally, when more than one field is optimized simultaneously, it is often the case that a set of parameter values that optimizes one field can degrade performance in another. Concepts from multiobjective optimization are used to quantify these tradeoffs

Quantifying uncertainty in precipitation climatology, twenty-first century change, and teleconnections in global climate models

The ability of global climate models (GCMs) to simulate climatological precipitation and other features of the hydrological cycle accurately is acceptable by some metrics, especially at large scales. Regionally, however, there can be substantial discrepancy in a multi-model ensemble, both in the annual or seasonal historical precipitation climatology as well as in end-of-century changes. Characterizing this intermodel spread and identifying leading uncertainty patterns and underlying physical pathways is important in constraining climatological biases and projections of future change. This dissertation looks at three aspects of precipitation uncertainty in ensembles.First, El Nino-Southern Oscillation (ENSO) teleconnections are analyzed in an atmosphere-only ensemble to gauge the ability of atmospheric components of GCMs to reproduce ENSO precipitation teleconnections. This serves as a test for how well models simulate the atmospheric response to sea surface temperature forcing in the immediate ENSO vicinity, as well as how accurately they reproduce the large-scale tropical-to-midlatitude dynamics leading to teleconnected precipitation. While individual models have difficulty in simulating the exact spatial pattern of teleconnections, they demonstrate skill in regional amplitude measures and sign agreement of the precipitation teleconnections at the grid point level, which lends value to the use of such measures in global warming projections.Next, objective spatial analysis techniques are applied to a fully-coupled GCM ensemble in order to visualize patterns of uncertainty in end-of-century precipitation changes and in the historical climatology. Global patterns are considered first, with the tropics exerting a clear dominance in intermodel spread, mainly within zones of deep convection or along convective margins. Regional domains are considered second, with a focus on the wintertime midlatitude Pacific storm track. A key region of end-of-century precipitation change uncertainty is identified at the terminus of the storm track, and large-scale circulation processes related to model differences in upper-level jet increases are found to play a role. These results help pinpoint a source of intermodel spread in projected precipitation changes along the North American west coast, especially for the Southern California region.Last, an existing perturbed physics ensemble is examined in order to understand the parameter sensitivity of climatological precipitation and other fields. This ensemble consists of integrations in which four parameters in the deep convection scheme were systematically varied. Models of parameter dependence are constructed for precipitation, and this process--termed metamodeling--is a computationally cheap alternative to brute-force sampling of parameter space in the GCM. A quadratic metamodel performs generally well but fails to capture sensitive regions of high nonlinearity for certain parameter ranges. A second metamodel is constructed by combining an approach from the engineering literature with the spatial uncertainty patterns used above, and it proves adept at capturing sensitive regions where its quadratic counterpart fails. Finally, when more than one field is optimized simultaneously, it is often the case that a set of parameter values that optimizes one field can degrade performance in another. Concepts from multiobjective optimization are used to quantify these tradeoffs

Why does Amazon precipitation decrease when tropical forests respond to increasing CO2?

Earth system models predict a zonal dipole of precipitation change over tropical South America, with decreases over the Amazon and increases over the Andes. Much of this has been attributed to the physiological response of the rainforest to elevated CO2, which describes a basin-wide reduction in stomatal conductance and transpiration. While robust in Earth system model experiments, details of the underlying atmospheric mechanism—specifically how it evolves in the context of land-atmosphere interaction and the diurnal cycle—are unresolved. We investigate this using idealized model simulations and find that within 24 hours of a CO2 increase, changes occur over the Amazon that engender synoptic timescale feedbacks. Decreased evapotranspiration from the rainforest throttles near-surface moisture, inducing a drier, warmer, and deeper boundary layer. Above this, enhanced turbulent diffusivity increases vapor in the lower free troposphere. Together, these processes reduce convective activity and cause immediate decreases in Amazon rainfall. Over the synoptic timescale, these changes leave behind lower tropospheric moisture, which is advected westward by the background jet and increases Andean precipitation. This produces a dipole of precipitation change consistent across global and regional models as well as parameterized and resolved convection, though details are sensitive to model topography and boundary layer formulation. The mechanism reported here stresses the importance of fast timescale processes affecting stability over a period of hours that can influence longer-term vegetation-climate interactions. These results help clarify the Amazon's physiological response to rising CO2 and provide insight into possible causes of historical model biases and end-of-century uncertainty in this region

Wildfire response to changing daily temperature extremes in California's Sierra Nevada.

Burned area has increased across California, especially in the Sierra Nevada range. Recent fires there have had devasting social, economic, and ecosystem impacts. To understand the consequences of new extremes in fire weather, here we quantify the sensitivity of wildfire occurrence and burned area in the Sierra Nevada to daily meteorological variables during 2001–2020. We find that the likelihood of fire occurrence increases nonlinearly with daily temperature during summer, with a 1°C increase yielding a 19 to 22% increase in risk. Area burned has a similar, nonlinear sensitivity, with 1°C of warming yielding a 22 to 25% increase in risk. Solely considering changes in summer daily temperatures from climate model projections, we estimate that by the 2040s, fire number will increase by 51 ± 32%, and burned area will increase by 59 ± 33%. These trends highlight the threat posed to fire management by hotter and drier summers

North American Climate in CMIP5 Experiments. Part II: Evaluation of Historical Simulations of Intraseasonal to Decadal Variability

This is the second part of a three-part paper on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) that evaluates the twentieth-century simulations of intraseasonal to multidecadal variability and teleconnections with North American climate. Overall, the multimodel ensemble does reasonably well at reproducing observed variability in several aspects, but it does less well at capturing observed teleconnections, with implications for future projections examined in part three of this paper. In terms of intraseasonal variability, almost half of the models examined can reproduce observed variability in the eastern Pacific and most models capture the midsummer drought over Central America. The multimode mean replicates the density of traveling tropical synoptic-scale disturbances but with large spread among the models. On the other hand, the coarse resolution of the models means that tropical cyclone frequencies are under predicted in the Atlantic and eastern North Pacific. The frequency and mean amplitude of ENSO are generally well reproduced, although teleconnections with North American climate are widely varying among models and only a few models can reproduce the east and central Pacific types of ENSO and connections with U.S. winter temperatures. The models capture the spatial pattern of Pacific decadal oscillation (PDO) variability and its influence on continental temperature and West Coast precipitation but less well for the wintertime precipitation. The spatial representation of the Atlantic multidecadal oscillation (AMO) is reasonable, but the magnitude of SST anomalies and teleconnections are poorly reproduced. Multidecadal trends such as the warming hole over the central–southeastern United States and precipitation increases are not replicated by the models, suggesting that observed changes are linked to natural variability

North American Climate in CMIP5 Experiments. Part I: Evaluation of Historical Simulations of Continental and Regional Climatology

This is the first part of a three-part paper on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) that evaluates the historical simulations of continental and regional climatology with a focus on a core set of 17models. The authors evaluate the models for a set of basic surface climate and hydrological variables and their extremes for the continent. This is supplemented by evaluations for selected regional climate processes relevant to North American climate, including cool season western Atlantic cyclones, the North American monsoon, the U.S. Great Plains low-level jet, and Arctic sea ice. In general, the multi-model ensemble mean represents the observed spatial patterns of basic climate and hydrological variables but with large variability across models and regions in the magnitude and sign of errors. No single model stands out as being particularly better or worse across all analyses, although some models consistently outperform the others for certain variables across most regions and seasons and higher-resolution models tend to perform better for regional processes. The CMIP5 multi-model ensemble shows a slight improvement relative to CMIP3 models in representing basic climate variables, in terms of the mean and spread, although performance has decreased for some models. Improvements in CMIP5 model performance are noticeable for some regional climate processes analyzed, such as the timing of the North American monsoon. The results of this paper have implications for the robustness of future projections of climate and its associated impacts, which are examined in the third part of the paper