arXiv subjects

Elisabeth J. Moyer

Publications and source records attributed to Elisabeth J. Moyer.

8 recordsLinked to original sources

arXiv2022

Robust Relationship Between Mid-latitudes CAPE and Moist Static Energy in Present and Future Simulations

Convective available potential energy (CAPE), a metric associated with severe weather, is expected to increase with warming. Under the most widely-accepted theory, developed for strongly convective regimes, mean CAPE should rise following the Clausius-Clapeyron (C-C) relationship at 6-7%/K. We show here that although the magnitude of CAPE change in high-resolution model output is only slightly underestimated with simple theories, it is insufficient to describe the distributional changes, which has a down-sloping structure and is crucial for impact assessment. A more appropriate framework for understanding CAPE changes uses the tight correlation between CAPE and moist static energy (MSE) surplus. Atmospheric profiles develop appreciable CAPE only when MSE surplus becomes positive; beyond this point, CAPE increases as $\sim$25% of the rise in MSE surplus. Because this relationship is robust across climate states, changes in future CAPE distributions can be well-captured by a simple scaling of present-day data using only three parameters.

physics.ao-ph↗

arXiv2020

Reanalyses and a high-resolution model fail to capture the `high tail' of CAPE distributions

Convective available potential energy (CAPE) is of strong interest in climate modeling because of its role in both severe weather and in model construction. Extreme levels of CAPE ($>$ 2000 J/kg) are associated with high-impact weather events, and CAPE is widely used in convective parametrizations to help determine the strength and timing of convection. However, to date no study has systematically evaluated CAPE biases in models in a climatological context, in an assessment large enough to characterize the high tail of the CAPE distribution. This work compares CAPE distributions in over 200,000 summertime proximity soundings from four sources: the observational radiosonde network (IGRA), 0.125 degree reanalysis (ERA-Interim and ERA5), and a 4 km convection-permitting regional WRF simulation driven by ERA-Interim. Both reanalyses and model consistently show too-narrow distributions of CAPE, with the high tail ($>$ 95th percentile) systematically biased low by up to 10% in surface-based CAPE and 20% at the most unstable layer. This "missing tail" corresponds to the most impacts-relevant conditions. CAPE bias in all datasets is driven by bias in surface temperature and humidity: reanalyses and model undersample observed cases of extreme heat and moisture. These results suggest that reducing inaccuracies in land surface and boundary layer models is critical for accurately reproducing CAPE.

physics.ao-ph↗

arXiv2017

Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking

Dynamical downscaling with high-resolution regional climate models may offer the possibility of realistically reproducing precipitation and weather events in climate simulations. As resolutions fall to order kilometers, the use of explicit rather than parametrized convection may offer even greater fidelity. However, these increased model resolutions both allow and require increasingly complex diagnostics for evaluating model fidelity. In this study we use a suite of dynamically downscaled simulations of the summertime U.S. (WRF driven by NCEP) with systematic variations in parameters and treatment of convection as a test case for evaluation of model precipitation. In particular, we use a novel rainstorm identification and tracking algorithm that allocates essentially all rainfall to individual precipitation events (Chang et al. 2016). This approach allows multiple insights, including that, at least in these runs, model wet bias is driven by excessive areal extent of precipitating events. Biases are time-dependent, producing excessive diurnal cycle amplitude. We show that this effect is produced not by new production of events but by excessive enlargement of long-lived precipitation events during daytime, and that in the domain average, precipitation biases appear best represented as additive offsets. Of all model configurations evaluated, convection-permitting simulations most consistently reduced biases in precipitation event characteristics.

stat.AP↗

arXiv2017

Estimating trends in the global mean temperature record

Given uncertainties in physical theory and numerical climate simulations, the historical temperature record is often used as a source of empirical information about climate change. Many historical trend analyses appear to deemphasize physical and statistical assumptions: examples include regression models that treat time rather than radiative forcing as the relevant covariate and time series methods that account for internal variability nonparametrically. However, given a limited record and the presence of internal variability, estimating radiatively forced historical temperature trends necessarily requires assumptions. Ostensibly empirical methods can involve an inherent conflict in assumptions: they require data records that are short enough for naive trend models to apply but long enough for internal variability to be accounted for. In the context of global mean temperatures, methods that deemphasize assumptions can therefore produce misleading inferences, because the twentieth century trend is complex and the scale of correlation is long relative to the data length. We illustrate how a simple but physically motivated trend model can provide better-fitting and more broadly applicable trend estimates and can address a wider array of questions. The model allows one to distinguish, within a single framework, between uncertainties in the shorter-term versus longer-term response to radiative forcing, with implications not only on historical trends but also on uncertainties in future projections. We also investigate the consequence on inferred uncertainties of the choice of a statistical description of internal variability. While nonparametric methods may seem to avoid making explicit assumptions, we demonstrate how even misspecified parametric methods, if attuned to important characteristics of internal variability, can result in more accurate statements about trend uncertainty.

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arXiv2016

Isotopic profiles imply strong convective influence on water near the tropical tropopause

The influence of deep convection on water vapor in the Tropical Tropopause Layer (TTL), the region just below the high ($\sim$18 km), cold tropical tropopause, remains an outstanding question in atmospheric science. Moisture transport to this region is important for climate projections because it drives the formation of local cirrus (ice) clouds, which have a disproportionate impact on the Earth's radiative balance. Deep cumulus towers carrying large volumes of ice are known to reach the TTL, but their importance to the water budget has been debated for several decades. We show here that profiles of the isotopic composition of water vapor can provide a quantitative estimate of the convective contribution to TTL moistening. Isotopic measurements from the ACE satellite instrument, in conjunction with ice loads inferred from CALIOP satellite measurements and simple mass-balance modeling, suggest that convection is the dominant source of water vapor in the TTL up to near-tropopause altitudes. The relatively large ice loads inferred from CALIOP satellite measurements can be produced only with significant water sources, and isotopic profiles imply that these sources are predominantly convective ice. Sublimating ice from deep convection appears to increase TTL cirrus by a factor of several over that expected if cirrus production were driven only by large-scale uplift; sensitivity analysis implies that these conclusions are robust for most physically reasonable assumptions. Changes in tropical deep convection in future warmer conditions may thus provide an important climate feedback.

physics.ao-ph↗

arXiv2016

Estimating changes in temperature extremes from millennial scale climate simulations using generalized extreme value (GEV) distributions

Changes in extreme weather may produce some of the largest societal impacts of anthropogenic climate change. However, it is intrinsically difficult to estimate changes in extreme events from the short observational record. In this work we use millennial runs from the CCSM3 in equilibrated pre-industrial and possible future conditions to examine both how extremes change in this model and how well these changes can be estimated as a function of run length. We estimate changes to distributions of future temperature extremes (annual minima and annual maxima) in the contiguous United States by fitting generalized extreme value (GEV) distributions. Using 1000-year pre-industrial and future time series, we show that the magnitude of warm extremes largely shifts in accordance with mean shifts in summertime temperatures. In contrast, cold extremes warm more than mean shifts in wintertime temperatures, but changes in GEV location parameters are largely explainable by mean shifts combined with reduced wintertime temperature variability. In addition, changes in the spread and shape of the GEV distributions of cold extremes at inland locations can lead to discernible changes in tail behavior. We then examine uncertainties that result from using shorter model runs. In principle, the GEV distribution provides theoretical justification to predict infrequent events using time series shorter than the recurrence frequency of those events. To investigate how well this approach works in practice, we estimate 20-, 50-, and 100-year extreme events using segments of varying lengths. We find that even using GEV distributions, time series that are of comparable or shorter length than the return period of interest can lead to very poor estimates. These results suggest caution when attempting to use short observational time series or model runs to infer infrequent extremes.

stat.AP↗

arXiv2016

Changes in Spatio-temporal Precipitation Patterns in Changing Climate Conditions

Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6-7%/K, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (1-2 %/K in the global average in transient runs). Some other aspect of precipitation events must then change to compensate for this difference. We develop here a new methodology for identifying individual rainstorms and studying their physical characteristics - including starting location, intensity, spatial extent, duration, and trajectory - that allows identifying that compensating mechanism. We apply this technique to precipitation over the contiguous U.S. from both radar-based data products and high-resolution model runs simulating 80 years of business-as-usual warming. In model studies, we find that the dominant compensating mechanism is a reduction of storm size. In summer, rainstorms become more intense but smaller, in winter, rainstorm shrinkage still dominates, but storms also become less numerous and shorter duration. These results imply that flood impacts from climate change will be less severe than would be expected from changes in precipitation intensity alone. We show also that projected changes are smaller than model-observation biases, implying that the best means of incorporating them into impact assessments is via "data-driven simulations" that apply model-projected changes to observational data. We therefore develop a simulation algorithm that statistically describes model changes in precipitation characteristics and adjusts data accordingly, and show that, especially for summertime precipitation, it outperforms simulation approaches that do not include spatial information.

stat.AP↗

arXiv2015

Temperatures in transient climates: improved methods for simulations with evolving temporal covariances

Future climate change impacts depend on temperatures not only through changes in their means but also through changes in their variability. General circulation models (GCMs) predict changes in both means and variability; however, GCM output should not be used directly as simulations for impacts assessments because GCMs do not fully reproduce present-day temperature distributions. This paper addresses an ensuing need for simulations of future temperatures that combine both the observational record and GCM projections of changes in means and temporal covariances. Our perspective is that such simulations should be based on transforming observations to account for GCM projected changes, in contrast to methods that transform GCM output to account for discrepancies with observations. Our methodology is designed for simulating transient (non-stationary) climates, which are evolving in response to changes in CO$_2$ concentrations (as is the Earth at present). This work builds on previously described methods for simulating equilibrium (stationary) climates. Since the proposed simulation relies on GCM projected changes in covariance, we describe a statistical model for the evolution of temporal covariances in a GCM under future forcing scenarios, and apply this model to an ensemble of runs from one GCM, CCSM3. We find that, at least in CCSM3, changes in the local covariance structure can be explained as a function of the regional mean change in temperature and the rate of change of warming. This feature means that the statistical model can be used to emulate the evolving covariance structure of GCM temperatures under scenarios for which the GCM has not been run. When combined with an emulator for mean temperature, our methodology can simulate evolving temperatures under such scenarios, in a way that accounts for projections of changes while still retaining fidelity with the observational record.

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