Global Climate Model (GCM) projections suggest that drought will increase across large areas of the globe, but lack skill at simulating climate variations at local-scales where adaptation decisions are made. As such, GCMs are often downscaled using statistical methods. This study develops a 3-step framework to assess the use of large-scale environmental patterns to assess local precipitation in statistically downscaling to local drought. In Step 1, two statistical downscaling models are developed: one based on temperature and precipitation and another based on temperature and a large-scale predictor that serves as a proxy for precipitation. A key component is identifying the large-scale predictor, which is customized for the location of interest. In Step 2, the statistical models are evaluated using NCEP/NCAR Reanalysis data. In Step 3, we apply a large ensemble of future GCM projections to the statistical models. The technique is demonstrated for predicting drought, as measured by the Palmer Drought Severity Index, in South-central Oklahoma, but the framework is general and applicable to other locations. Case study results using the Reanalysis show that the large-scale predictor explains slightly more variance than precipitation when predicting local drought. Applying future GCM projections to both statistical models indicates similar drying trends, but demonstrates notable internal variability. The case study demonstrates: (1) where a large-scale predictor performs comparably (or better) than precipitation directly, then it is an appealing predictor choice to use with future projections, (2) when statistically downscaling to local scales, it is critical to consider internal variability, as it may be more important than predictor selection.
Abstract The 2012 drought was the most severe and extensive summertime U.S. drought in half a century with substantial economic loss and impacts on food security and commodity prices. A unique aspect of the 2012 drought was its rapid onset and intensification over the Southern Rockies, extending to the Great Plains during late spring and early summer, and the absence of known precursor large‐scale patterns. Drought prediction therefore remains a major challenge. This study evaluates relationships among snow, soil moisture, and precipitation to identify sources of potential predictability of the 2012 summer drought using observations and a Weather Research and Forecasting model multiphysics ensemble experiment. Although underestimated in intensity, the drought signal is robust to the way atmospheric physical processes are represented in the model. For the Southern Rockies, soil moisture exhibits stronger persistence than precipitation in observations and the ensemble experiment. Correlations between winter/spring snowmelt and concurrent and following season soil moisture, and between soil moisture and concurrent and following season precipitation, in both observations and the model ensemble, suggest potential predictability beyond 1 and 2 month lead‐time reside in the land surface conditions for apparent flash droughts such as the 2012 drought.
Abstract Decadal prediction is a relatively new branch of climate science that bridges the gap between seasonal climate forecasts and multidecadal-to-century projections of climate change. This paper develops a three-step framework toward the potential application of decadal temperature predictions using the Community Climate System Model, version 4 (CCSM4). In step 1, the predictions are evaluated and it is found that the temperature hindcasts show skill over some regions of the United States and Canada. In step 2, the predictions are manipulated using two methods: a deterministic-anomaly approach (like climate change projections) and a probabilistic tercile-based approach (like seasonal forecasts). In step 3, the predictions are translated by adding a delta (for the anomaly manipulation) and conducting a weighted resample (for the probabilistic manipulation), as well as using a new hybrid method. Using the 2010 initialized hindcast, the framework is demonstrated for predicting 2011–15 over two case-study watersheds [Ottawa (Canada) and Colorado]. For the Colorado watershed, there was a noticeable shift toward higher temperatures, and the delta, weighted resample, and hybrid translations all were better at capturing the observed temperatures than was an approach that used climatological values. For the Ottawa watershed, the observed temperatures over the period of prediction were only subtly different than the climatological values; therefore, the difference between the translation methods was less noticeable. The advantages and disadvantages of the manipulation and translation approaches are discussed, as well as how their use will depend on the user context. The authors emphasize that skill evaluations should be tailored to particular applications and identify additional steps that are needed before the decadal temperature predictions can be readily incorporated into applications.
Abstract. Drought is a function of both natural and human influences, but fully characterizing the interactions between human and natural influences on drought remains challenging. To better characterize parts of the drought feedback loop, this study combines hydrological and societal perspectives to characterize and quantify the potential for drought action. For the hydrological perspective, we examine historical groundwater data, from which we determine the decadal likelihoods of exceeding hydrologic thresholds relevant to different water uses. Stakeholder interviews yield data about how people rate the importance of water for different water uses. We combine these to quantify the Potential Drought Action Indicator (PDAI). The PDAI is demonstrated for a study site in south-central Oklahoma, where water availability is highly influenced by drought and management of water resources is contested by local stakeholders. For the hydrological perspective, we find that the historical decadal likelihood of exceedance for a moderate threshold associated with municipal supply has ranged widely: from 23 % to 75 %, which corresponds well with natural drought variability in the region. For the societal perspective, stakeholder interviews reveal that people value water differently for various uses. Combining this information into the PDAI illustrates that potential drought action increases as the hydrologic threshold is exceeded more often; this occurs as conditions get drier and when water use thresholds are more moderate. The PDAI also shows that for water uses where stakeholders have diverse views of importance, the PDAI will be diverse as well, and this is exacerbated under drier conditions. The variability in stakeholder views of importance is partially explained by stakeholders' cultural worldviews, pointing to some implications for managing water when drought risks threaten. We discuss how the results can be used to reduce potential disagreement among stakeholders and promote sustainable water management, which is particularly important for planning under increasing drought.
Abstract Weather Research and Forecasting (WRF) model simulations are performed over Russia for July and December 2005, 2006, and 2007 to create a “dataset” to assess the impact of network density and design on regional averages. Based on the values at all WRF grid points, regional averages for various quantities are calculated for 2.8° × 2.8° areas as the “reference.” Regional averages determined based on 40 artificial networks and 411 “sites” that correspond to the locations of a real network are compared with the reference regional averages. The 40 networks encompass 10 networks of 500, 400, 200, or 100 different randomly taken WRF grid points as sites. The real network’s site distribution misrepresents the landscape. This misrepresentation leads to errors in regional averages that show geographical and temporal trends for most quantities: errors are lower over shores of large lakes than coasts and lowest over flatland followed by low and high mountain ranges; offsets in timing occur during frontal passages when several sites are passed at nearly the same time. Generally, the real network underestimates regional averages of sea level pressure, wind speed, and precipitation over Russia up to 4.8 hPa (4.8 hPa), 0.7 m s−1 (0.5 m s−1), and 0.2 mm day−1 and overestimates regional averages of 2-m temperature, downward shortwave radiation, and soil temperature over Russia up to 1.9 K (1.4 K), 19 W m−2 (14 W m−2), and 1.5 K (1.8 K) in July (December). The low density of the ten 100-site networks causes difficulties for sea level pressure. Regional averages obtained from the 30 networks with 200 or more randomly distributed sites represent the reference regional averages, trends, and variability for all quantities well.
An index of North Atlantic tropical cyclone (TC) damage potential due to winds and coastal surge is developed using seasonal climate variables of relative sea surface temperature and steering flow. These climate variables are proxies for the key damaging TC parameters of intensity, size, and forward speed that constitute an existing cyclone damage potential index. This climate-based approach has the advantage of sidestepping the need for data on individual TCs and explains 48 % of the variance in historical cyclone damage potential. The merit of the cyclone damage potential is in assessments relative to past events or past periods, and may be translated to actual damage using relationships between the damage potential index and specific exposure and vulnerability characteristics. Spread in the change in damage potential over the 21st century among climate simulations under representative concentration pathways 4.5, 6.0, and 8.5 is found to be less than the spread due to internal variability, as assessed using a climate model initial condition large ensemble. This study highlights the importance of accounting for internal climate variability in future climate impact assessments.
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