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• 1. [Article] Impact of Soil Moisture–Atmosphere Interactions on Surface Temperature Distribution

  Understanding how different physical processes can shape the probability distribution function (PDF) of surface temperature, in particular the tails of the distribution, is essential for the attribution ...

  Citation × Citation Citation Abstract Copy Title: Impact of Soil Moisture–Atmosphere Interactions on Surface Temperature Distribution Author: Berg, Alexis, Lintner, Benjamin R., Findell, Kirsten L., Malyshev, Sergey, Loikith, Paul C., Gentine, Pierre Year: 2014 Understanding how different physical processes can shape the probability distribution function (PDF) of surface temperature, in particular the tails of the distribution, is essential for the attribution and projection of future extreme temperature events. In this study, the contribution of soil moisture–atmosphere interactions to surface temperature PDFs is investigated. Soil moisture represents a key variable in the coupling of the land and atmosphere, since it controls the partitioning of available energy between sensible and latent heat flux at the surface. Consequently, soil moisture variability driven by the atmosphere may feed back onto the near-surface climate—in particular, temperature. In this study, two simulations of the current-generation Geophysical Fluid Dynamics Laboratory (GFDL) Earth System Model, with and without interactive soil moisture, are analyzed in order to assess how soil moisture dynamics impact the simulated climate. Comparison of these simulations shows that soil moisture dynamics enhance both temperature mean and variance over regional ‘‘hotspots’’ of land–atmosphere coupling.Moreover, higher-order distribution moments, such as skewness and kurtosis, are also significantly impacted, suggesting an asymmetric impact on the positive and negative extremes of the temperature PDF. Such changes are interpreted in the context of altered distributions of the surface turbulent and radiative fluxes. That the moments of the temperature distribution may respond differentially to soil moisture dynamics underscores the importance of analyzing moments beyond the mean and variance to characterize fully the interplay of soil moisture and near-surface temperature. In addition, it is shown that soil moisture dynamics impacts daily temperature variability at different time scales over different regions in the model. Title: Impact of Soil Moisture–Atmosphere Interactions on Surface Temperature Distribution Author: Berg, Alexis, Lintner, Benjamin R., Findell, Kirsten L., Malyshev, Sergey, Loikith, Paul C., Gentine, Pierre Year: 2014 Understanding how different physical processes can shape the probability distribution function (PDF) of surface temperature, in particular the tails of the distribution, is essential for the attribution and projection of future extreme temperature events. In this study, the contribution of soil moisture–atmosphere interactions to surface temperature PDFs is investigated. Soil moisture represents a key variable in the coupling of the land and atmosphere, since it controls the partitioning of available energy between sensible and latent heat flux at the surface. Consequently, soil moisture variability driven by the atmosphere may feed back onto the near-surface climate—in particular, temperature. In this study, two simulations of the current-generation Geophysical Fluid Dynamics Laboratory (GFDL) Earth System Model, with and without interactive soil moisture, are analyzed in order to assess how soil moisture dynamics impact the simulated climate. Comparison of these simulations shows that soil moisture dynamics enhance both temperature mean and variance over regional ‘‘hotspots’’ of land–atmosphere coupling.Moreover, higher-order distribution moments, such as skewness and kurtosis, are also significantly impacted, suggesting an asymmetric impact on the positive and negative extremes of the temperature PDF. Such changes are interpreted in the context of altered distributions of the surface turbulent and radiative fluxes. That the moments of the temperature distribution may respond differentially to soil moisture dynamics underscores the importance of analyzing moments beyond the mean and variance to characterize fully the interplay of soil moisture and near-surface temperature. In addition, it is shown that soil moisture dynamics impacts daily temperature variability at different time scales over different regions in the model. Close

• 2. [Article] The Influence of Recurrent Modes of Climate Variability on the Occurrence of Winter and Summer Extreme Temperatures over North America

  The influence of the Pacific–North American (PNA) pattern, the northern annular mode (NAM), and the El Ni~no–Southern Oscillation (ENSO) on extreme temperature days and months over North America is examined. ...

  Citation × Citation Citation Abstract Copy Title: The Influence of Recurrent Modes of Climate Variability on the Occurrence of Winter and Summer Extreme Temperatures over North America Author: Loikith, Paul C., Broccoli, Anthony J. Year: 2014 The influence of the Pacific–North American (PNA) pattern, the northern annular mode (NAM), and the El Ni~no–Southern Oscillation (ENSO) on extreme temperature days and months over North America is examined. Associations between extreme temperature days and months are strongest with the PNA and NAM andweaker for ENSO. In general, the associationwith extremes tends to be stronger onmonthly than daily time scales and for winter as compared to summer. Extreme temperatures are associated with the PNAandNAMin the vicinity of the centers of action of these circulation patterns; however, many extremes also occur on days when the amplitude and polarity of these patterns do not favor their occurrence. In winter, synoptic-scale, transient weather disturbances are important drivers of extreme temperature days; however, many of these smaller-scale events are concurrent with amplified PNA or NAMpatterns. Associations are weaker in summer when other physicalmechanisms affecting the surface energy balance, such as anomalous soilmoisture content, also influence the occurrence of extreme temperatures. Title: The Influence of Recurrent Modes of Climate Variability on the Occurrence of Winter and Summer Extreme Temperatures over North America Author: Loikith, Paul C., Broccoli, Anthony J. Year: 2014 The influence of the Pacific–North American (PNA) pattern, the northern annular mode (NAM), and the El Ni~no–Southern Oscillation (ENSO) on extreme temperature days and months over North America is examined. Associations between extreme temperature days and months are strongest with the PNA and NAM andweaker for ENSO. In general, the associationwith extremes tends to be stronger onmonthly than daily time scales and for winter as compared to summer. Extreme temperatures are associated with the PNAandNAMin the vicinity of the centers of action of these circulation patterns; however, many extremes also occur on days when the amplitude and polarity of these patterns do not favor their occurrence. In winter, synoptic-scale, transient weather disturbances are important drivers of extreme temperature days; however, many of these smaller-scale events are concurrent with amplified PNA or NAMpatterns. Associations are weaker in summer when other physicalmechanisms affecting the surface energy balance, such as anomalous soilmoisture content, also influence the occurrence of extreme temperatures. Close

• 3. [Article] Short-tailed temperature distributions over North America and Implications for Future Changes in Extremes

  Some regions of North America exhibit nonnormal temperature distributions. Shorter-than-Gaussian warm tails are a special subset of these cases, with potentially meaningful implications for future changes ...

  Citation × Citation Citation Abstract Copy Title: Short-tailed temperature distributions over North America and Implications for Future Changes in Extremes Author: Loikith, Paul C., Neelin, J. David Year: 2015 Some regions of North America exhibit nonnormal temperature distributions. Shorter-than-Gaussian warm tails are a special subset of these cases, with potentially meaningful implications for future changes in extreme warm temperatures under anthropogenic global warming. Locations exhibiting shorter-than-Gaussian warm tails would experience a greater increase in extreme warm temperature exceedances than a location with a Gaussian or long warm-side tail under a simple uniform warm shift in the distribution. Here we identify regions exhibiting such behavior over North America and demonstrate the effect of a simple warm shift on changes in extreme warm temperature exceedances. Some locations exceed the 95th percentile of the original distribution by greater than 40% of the time after this uniform shift. While the manner in which distributions change under global warming may be more complex than a simple shift, these results provide an observational baseline for climate model evaluation. Title: Short-tailed temperature distributions over North America and Implications for Future Changes in Extremes Author: Loikith, Paul C., Neelin, J. David Year: 2015 Some regions of North America exhibit nonnormal temperature distributions. Shorter-than-Gaussian warm tails are a special subset of these cases, with potentially meaningful implications for future changes in extreme warm temperatures under anthropogenic global warming. Locations exhibiting shorter-than-Gaussian warm tails would experience a greater increase in extreme warm temperature exceedances than a location with a Gaussian or long warm-side tail under a simple uniform warm shift in the distribution. Here we identify regions exhibiting such behavior over North America and demonstrate the effect of a simple warm shift on changes in extreme warm temperature exceedances. Some locations exceed the 95th percentile of the original distribution by greater than 40% of the time after this uniform shift. While the manner in which distributions change under global warming may be more complex than a simple shift, these results provide an observational baseline for climate model evaluation. Close

• 4. [Article] Surface Temperature Probability Distributions in the NARCCAP Hindcast Experiment: Evaluation Methodology, Metrics, and Results

  Methodology is developed and applied to evaluate the characteristics of daily surface temperature distributions in a six-member regional climate model (RCM) hindcast experiment conducted as part of the ...

  Citation × Citation Citation Abstract Copy Title: Surface Temperature Probability Distributions in the NARCCAP Hindcast Experiment: Evaluation Methodology, Metrics, and Results Author: Loikith, Paul C., Waliser, Duane E., Lee, Huikyo, Kim, Jinwon, Neelin, J. David, Lintner, Benjamin R., McGinnis, Seth, Mattmann, Chris A., Mears, Linda O. Year: 2015 Methodology is developed and applied to evaluate the characteristics of daily surface temperature distributions in a six-member regional climate model (RCM) hindcast experiment conducted as part of the North American Regional Climate Change Assessment Program (NARCCAP). A surface temperature dataset combining gridded station observations and reanalysis is employed as the primary reference. Temperature biases are documented across the distribution, focusing on the median and tails. Temperature variance is generally higher in the RCMs than reference, while skewness is reasonably simulated in winter over the entire domain and over the western United States and Canada in summer. Substantial differences in skewness exist over the southern and eastern portions of the domain in summer. Four examples with observed long-tailed probability distribution functions (PDFs) are selected for model comparison. Long cold tails in the winter are simulated with high fidelity for Seattle, Washington, and Chicago, Illinois. In summer, theRCMs are unable to capture the distribution width and long warm tails for the coastal location of Los Angeles, California, while long cold tails are poorly realized for Houston, Texas. The evaluation results are repeated using two additional reanalysis products adjusted by station observations and two standard reanalysis products to assess the impact of observational uncertainty. Results are robust when compared with those obtained using the adjusted reanalysis products as reference, while larger uncertainties are introduced when standard reanalysis is employed as reference. Model biases identified in this work will allow for further investigation into associated mechanisms and implications for future simulations of temperature extremes. Title: Surface Temperature Probability Distributions in the NARCCAP Hindcast Experiment: Evaluation Methodology, Metrics, and Results Author: Loikith, Paul C., Waliser, Duane E., Lee, Huikyo, Kim, Jinwon, Neelin, J. David, Lintner, Benjamin R., McGinnis, Seth, Mattmann, Chris A., Mears, Linda O. Year: 2015 Methodology is developed and applied to evaluate the characteristics of daily surface temperature distributions in a six-member regional climate model (RCM) hindcast experiment conducted as part of the North American Regional Climate Change Assessment Program (NARCCAP). A surface temperature dataset combining gridded station observations and reanalysis is employed as the primary reference. Temperature biases are documented across the distribution, focusing on the median and tails. Temperature variance is generally higher in the RCMs than reference, while skewness is reasonably simulated in winter over the entire domain and over the western United States and Canada in summer. Substantial differences in skewness exist over the southern and eastern portions of the domain in summer. Four examples with observed long-tailed probability distribution functions (PDFs) are selected for model comparison. Long cold tails in the winter are simulated with high fidelity for Seattle, Washington, and Chicago, Illinois. In summer, theRCMs are unable to capture the distribution width and long warm tails for the coastal location of Los Angeles, California, while long cold tails are poorly realized for Houston, Texas. The evaluation results are repeated using two additional reanalysis products adjusted by station observations and two standard reanalysis products to assess the impact of observational uncertainty. Results are robust when compared with those obtained using the adjusted reanalysis products as reference, while larger uncertainties are introduced when standard reanalysis is employed as reference. Model biases identified in this work will allow for further investigation into associated mechanisms and implications for future simulations of temperature extremes. Close

• 5. [Article] Classifying Reanalysis Surface Temperature Probability Density Functions (PDFs) over North America with Cluster Analysis

  An important step in projecting future climate change impacts on extremes involves quantifying the underlying probability distribution functions (PDFs) of climate variables. However, doing so can prove ...

  Citation × Citation Citation Abstract Copy Title: Classifying Reanalysis Surface Temperature Probability Density Functions (PDFs) over North America with Cluster Analysis Author: Loikith, Paul C., Lintner, Benjamin R., Kim, Jinwon, Lee, Huikyo Year: 2013 An important step in projecting future climate change impacts on extremes involves quantifying the underlying probability distribution functions (PDFs) of climate variables. However, doing so can prove challenging when multiple models and large domains are considered. Here an approach to PDF quantification using k-means clustering is considered. A standard clustering algorithm (with k = 5 clusters) is applied to 33 years of daily January surface temperature from two state-of-the-art reanalysis products, the North American Regional Reanalysis and the Modern Era Retrospective Analysis for Research and Applications. The resulting cluster assignments yield spatially coherent patterns that can be broadly related to distinct climate regimes over North America, e.g., low variability over the tropical oceans or temperature advection across stronger or weaker gradients. This technique has the potential to be a useful and intuitive tool for evaluation of model-simulated PDF structure and could provide insight into projections of future changes in temperature. Title: Classifying Reanalysis Surface Temperature Probability Density Functions (PDFs) over North America with Cluster Analysis Author: Loikith, Paul C., Lintner, Benjamin R., Kim, Jinwon, Lee, Huikyo Year: 2013 An important step in projecting future climate change impacts on extremes involves quantifying the underlying probability distribution functions (PDFs) of climate variables. However, doing so can prove challenging when multiple models and large domains are considered. Here an approach to PDF quantification using k-means clustering is considered. A standard clustering algorithm (with k = 5 clusters) is applied to 33 years of daily January surface temperature from two state-of-the-art reanalysis products, the North American Regional Reanalysis and the Modern Era Retrospective Analysis for Research and Applications. The resulting cluster assignments yield spatially coherent patterns that can be broadly related to distinct climate regimes over North America, e.g., low variability over the tropical oceans or temperature advection across stronger or weaker gradients. This technique has the potential to be a useful and intuitive tool for evaluation of model-simulated PDF structure and could provide insight into projections of future changes in temperature. Close

• 6. [Article] Evaluation of Large‑Scale Meteorological Patterns Associated with Temperature Extremes in the NARCCAP Regional Climate Model Simulations

  Large-scale meteorological patterns (LSMPs) associated with temperature extremes are evaluated in a suite of regional climate model (RCM) simulations contributing to the North American Regional Climate ...

  Citation × Citation Citation Abstract Copy Title: Evaluation of Large‑Scale Meteorological Patterns Associated with Temperature Extremes in the NARCCAP Regional Climate Model Simulations Author: Loikith, Paul C., Waliser, Duane E., Lee, Huikyo, Neelin, J. David, Lintner, Benjamin, McGinnis, Seth, Mears, Linda, Kim, Jinwon Year: 2015 Large-scale meteorological patterns (LSMPs) associated with temperature extremes are evaluated in a suite of regional climate model (RCM) simulations contributing to the North American Regional Climate Change Assessment Program. LSMPs are characterized through composites of surface air temperature, sea level pressure, and 500 hPa geopotential height anomalies concurrent with extreme temperature days. Six of the seventeen RCM simulations are driven by boundary conditions from reanalysis while the other eleven are driven by one of four global climate models (GCMs). Four illustrative case studies are analyzed in detail. Model fidelity in LSMP spatial representation is high for cold winter extremes near Chicago. Winter warm extremes are captured by most RCMs in northern California, with some notable exceptions. Model fidelity is lower for cool summer days near Houston and extreme summer heat events in the Ohio Valley. Physical interpretation of these patterns and identification of wellsimulated cases, such as for Chicago, boosts confidence in the ability of these models to simulate days in the tails of the temperature distribution. Results appear consistent with the expectation that the ability of an RCM to reproduce a realistically shaped frequency distribution for temperature, especially at the tails, is related to its fidelity in simulating LMSPs. Each ensemble member is ranked for its ability to reproduce LSMPs associated with observed warm and cold extremes, identifying systematically high performing RCMs and the GCMs that provide superior boundary forcing. The methodology developed here provides a framework for identifying regions where further process-based evaluation would improve the understanding of simulation error and help guide future model improvement and downscaling efforts. Title: Evaluation of Large‑Scale Meteorological Patterns Associated with Temperature Extremes in the NARCCAP Regional Climate Model Simulations Author: Loikith, Paul C., Waliser, Duane E., Lee, Huikyo, Neelin, J. David, Lintner, Benjamin, McGinnis, Seth, Mears, Linda, Kim, Jinwon Year: 2015 Large-scale meteorological patterns (LSMPs) associated with temperature extremes are evaluated in a suite of regional climate model (RCM) simulations contributing to the North American Regional Climate Change Assessment Program. LSMPs are characterized through composites of surface air temperature, sea level pressure, and 500 hPa geopotential height anomalies concurrent with extreme temperature days. Six of the seventeen RCM simulations are driven by boundary conditions from reanalysis while the other eleven are driven by one of four global climate models (GCMs). Four illustrative case studies are analyzed in detail. Model fidelity in LSMP spatial representation is high for cold winter extremes near Chicago. Winter warm extremes are captured by most RCMs in northern California, with some notable exceptions. Model fidelity is lower for cool summer days near Houston and extreme summer heat events in the Ohio Valley. Physical interpretation of these patterns and identification of wellsimulated cases, such as for Chicago, boosts confidence in the ability of these models to simulate days in the tails of the temperature distribution. Results appear consistent with the expectation that the ability of an RCM to reproduce a realistically shaped frequency distribution for temperature, especially at the tails, is related to its fidelity in simulating LMSPs. Each ensemble member is ranked for its ability to reproduce LSMPs associated with observed warm and cold extremes, identifying systematically high performing RCMs and the GCMs that provide superior boundary forcing. The methodology developed here provides a framework for identifying regions where further process-based evaluation would improve the understanding of simulation error and help guide future model improvement and downscaling efforts. Close

• 7. [Article] Can Significant Trends be Detected in Surface Air Temperature and Precipitation Over South America in Recent Decades?

  Trends in near-surface air temperature and precipitation over South America are examined for the periods 1975–2004 and 1955–2004, respectively, using multiple observational and climate model data sets. ...

  Citation × Citation Citation Abstract Copy Title: Can Significant Trends be Detected in Surface Air Temperature and Precipitation Over South America in Recent Decades? Author: de Barros Soares, Daniel, Lee, Huikyo, Loikith, Paul C., Barkhordarian, Armineh, Mechoso, Carlos R. Year: 2016 Trends in near-surface air temperature and precipitation over South America are examined for the periods 1975–2004 and 1955–2004, respectively, using multiple observational and climate model data sets. The results for observed near-surface air temperature show an overall warming trend over much of the continent, with the largest magnitudes over central Brazil. These observed trends are found to be statistically significant using pre-industrial control simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) as the baseline to estimate natural climate variability. The observed trends are compared with those obtained in natural-only CMIP5 simulations, in which only natural forcings (i.e. volcanoes and solar variability) are included, and in historical CMIP5 simulations, in which anthropogenic forcings (i.e. changes in the atmospheric composition) are further incorporated. The historical CMIP5 simulations are more successful in capturing the observed temperature trends than the simulations with natural forcings only. It is suggested that anthropogenic warming is already evident over much of South America. Unlike the warming trends, observed precipitation trends over South America are less spatially coherent with both negative and positive values across the continent. Significant positive trends are found over South America in only one of the data sets used, and over a region that roughly encompasses the southern part of La Plata Basin (southern Brazil, Uruguay, and northeastern Argentina) in all data sets used. The historical CMIP5 simulations do not capture this feature. No firm conclusions are reached, therefore, for anthropogenic influences on precipitation changes in the period selected for study. Title: Can Significant Trends be Detected in Surface Air Temperature and Precipitation Over South America in Recent Decades? Author: de Barros Soares, Daniel, Lee, Huikyo, Loikith, Paul C., Barkhordarian, Armineh, Mechoso, Carlos R. Year: 2016 Trends in near-surface air temperature and precipitation over South America are examined for the periods 1975–2004 and 1955–2004, respectively, using multiple observational and climate model data sets. The results for observed near-surface air temperature show an overall warming trend over much of the continent, with the largest magnitudes over central Brazil. These observed trends are found to be statistically significant using pre-industrial control simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) as the baseline to estimate natural climate variability. The observed trends are compared with those obtained in natural-only CMIP5 simulations, in which only natural forcings (i.e. volcanoes and solar variability) are included, and in historical CMIP5 simulations, in which anthropogenic forcings (i.e. changes in the atmospheric composition) are further incorporated. The historical CMIP5 simulations are more successful in capturing the observed temperature trends than the simulations with natural forcings only. It is suggested that anthropogenic warming is already evident over much of South America. Unlike the warming trends, observed precipitation trends over South America are less spatially coherent with both negative and positive values across the continent. Significant positive trends are found over South America in only one of the data sets used, and over a region that roughly encompasses the southern part of La Plata Basin (southern Brazil, Uruguay, and northeastern Argentina) in all data sets used. The historical CMIP5 simulations do not capture this feature. No firm conclusions are reached, therefore, for anthropogenic influences on precipitation changes in the period selected for study. Close

• 8. [Article] Comparison between Observed and Model-Simulated Atmospheric CirculationPatterns Associated with Extreme Temperature Days over North AmericaUsing CMIP5 Historical Simulations

  Circulation patterns associated with extreme temperature days over North America, as simulated by a suite of climate models, are compared with those obtained from observations. The authors analyze 17 coupled ...

  Citation × Citation Citation Abstract Copy Title: Comparison between Observed and Model-Simulated Atmospheric CirculationPatterns Associated with Extreme Temperature Days over North AmericaUsing CMIP5 Historical Simulations Author: Loikith, Paul C., Broccoli, Anthony J. Year: 2015 Circulation patterns associated with extreme temperature days over North America, as simulated by a suite of climate models, are compared with those obtained from observations. The authors analyze 17 coupled atmosphere–ocean general circulation models contributing to the fifth phase of the Coupled Model Intercomparison Project. Circulation patterns are defined as composites of anomalies in sea level pressure and 500-hPa geopotential height concurrent with days in the tails of temperature distribution. Several metrics used to systematically describe circulation patterns associated with extreme temperature days are applied to both the observed and model-simulated data. Additionally, self-organizing maps are employed as a means of comparing observed and model-simulated circulation patterns across the North American domain. In general, the multimodel ensemble resembles the observed patterns well, especially in areas removed from complex geographic features (e.g., mountains and coastlines). Individual model results vary; however, the majority of models capture the major features observed. The multimodel ensemble captures several key features, including regional variations in the strength and orientation of atmospheric circulation patterns associated with extreme temperatures, both near the surface and aloft, as well as variations with latitude and season. The results from this work suggest that these models can be used to comprehensively examine the role that changes in atmospheric circulation will play in projected changes in temperature extremes because of future anthropogenic climate warming. Title: Comparison between Observed and Model-Simulated Atmospheric CirculationPatterns Associated with Extreme Temperature Days over North AmericaUsing CMIP5 Historical Simulations Author: Loikith, Paul C., Broccoli, Anthony J. Year: 2015 Circulation patterns associated with extreme temperature days over North America, as simulated by a suite of climate models, are compared with those obtained from observations. The authors analyze 17 coupled atmosphere–ocean general circulation models contributing to the fifth phase of the Coupled Model Intercomparison Project. Circulation patterns are defined as composites of anomalies in sea level pressure and 500-hPa geopotential height concurrent with days in the tails of temperature distribution. Several metrics used to systematically describe circulation patterns associated with extreme temperature days are applied to both the observed and model-simulated data. Additionally, self-organizing maps are employed as a means of comparing observed and model-simulated circulation patterns across the North American domain. In general, the multimodel ensemble resembles the observed patterns well, especially in areas removed from complex geographic features (e.g., mountains and coastlines). Individual model results vary; however, the majority of models capture the major features observed. The multimodel ensemble captures several key features, including regional variations in the strength and orientation of atmospheric circulation patterns associated with extreme temperatures, both near the surface and aloft, as well as variations with latitude and season. The results from this work suggest that these models can be used to comprehensively examine the role that changes in atmospheric circulation will play in projected changes in temperature extremes because of future anthropogenic climate warming. Close

• 9. [Article] Utilizing Humidity and Temperature Data to Advance Monitoring and Prediction of Meteorological Drought

  The fraction of land area over the Continental United States experiencing extreme hot and dry conditions has been increasing over the past several decades, consistent with expectation from anthropogenic ...

  Citation × Citation Citation Abstract Copy Title: Utilizing Humidity and Temperature Data to Advance Monitoring and Prediction of Meteorological Drought Author: Behrangi, Ali, Loikith, Paul C., Fetzer, Eric J., Nguyen, Hai M., Granger, Stephanie L. Year: 2015 The fraction of land area over the Continental United States experiencing extreme hot and dry conditions has been increasing over the past several decades, consistent with expectation from anthropogenic climate change. A clear concurrent change in precipitation, however, has not been confirmed. Vapor pressure deficit (VPD), combining temperature and humidity, is utilized here as an indicator of the background atmospheric conditions associated with meteorological drought. Furthermore, atmospheric conditions associated with warm season drought events are assessed by partitioning associated VPD anomalies into the temperature and humidity components. This approach suggests that the concurrence of anomalously high temperature and low humidity was an important driver of the rapid development and evolution of the exceptionally severe 2011 Texas and the 2012 Great Plains droughts. By classification of a decade of extreme drought events and tracking them back in time, it was found that near surface atmospheric temperature and humidity add essential information to the commonly used precipitation-based drought indicators and can advance efforts to determine the timing of drought onset and its severity. Title: Utilizing Humidity and Temperature Data to Advance Monitoring and Prediction of Meteorological Drought Author: Behrangi, Ali, Loikith, Paul C., Fetzer, Eric J., Nguyen, Hai M., Granger, Stephanie L. Year: 2015 The fraction of land area over the Continental United States experiencing extreme hot and dry conditions has been increasing over the past several decades, consistent with expectation from anthropogenic climate change. A clear concurrent change in precipitation, however, has not been confirmed. Vapor pressure deficit (VPD), combining temperature and humidity, is utilized here as an indicator of the background atmospheric conditions associated with meteorological drought. Furthermore, atmospheric conditions associated with warm season drought events are assessed by partitioning associated VPD anomalies into the temperature and humidity components. This approach suggests that the concurrence of anomalously high temperature and low humidity was an important driver of the rapid development and evolution of the exceptionally severe 2011 Texas and the 2012 Great Plains droughts. By classification of a decade of extreme drought events and tracking them back in time, it was found that near surface atmospheric temperature and humidity add essential information to the commonly used precipitation-based drought indicators and can advance efforts to determine the timing of drought onset and its severity. Close

• 10. [Article] Characterizing Large-Scale Meteorological Patterns and Associated Temperature and Precipitation Extremes over the Northwestern United States Using Self-Organizing Maps

  The self-organizing maps (SOMs) approach is demonstrated as a way to identify a range of archetypal large-scale meteorological patterns (LSMPs) over the northwestern United States and connect these patterns ...

  Citation × Citation Citation Abstract Copy Title: Characterizing Large-Scale Meteorological Patterns and Associated Temperature and Precipitation Extremes over the Northwestern United States Using Self-Organizing Maps Author: Loikith, Paul C., Lintner, Benjamin R., Sweeney, Alex Year: 2017 The self-organizing maps (SOMs) approach is demonstrated as a way to identify a range of archetypal large-scale meteorological patterns (LSMPs) over the northwestern United States and connect these patterns with local-scale temperature and precipitation extremes. SOMs are used to construct a set of 12 characteristic LSMPs (nodes) based on daily reanalysis circulation fields spanning the range of observed synoptic-scale variability for the summer and winter seasons for the period 1979–2013. Composites of surface variables are constructed for subsets of days assigned to each node to explore relationships between temperature, precipitation, and the node patterns. The SOMs approach also captures interannual variability in daily weather regime frequency related to El Niño–Southern Oscillation. Temperature and precipitation extremes in high-resolution gridded observations and in situ station data show robust relationships with particular nodes in many cases, supporting the approach as a way to identify LSMPs associated with local extremes. Assigning days from the extreme warm summer of 2015 and wet winter of 2016 to nodes illustrates how SOMs may be used to assess future changes in extremes. These results point to the applicability of SOMs to climate model evaluation and assessment of future projections of local-scale extremes without requiring simulations to reliably resolve extremes at high spatial scales. Title: Characterizing Large-Scale Meteorological Patterns and Associated Temperature and Precipitation Extremes over the Northwestern United States Using Self-Organizing Maps Author: Loikith, Paul C., Lintner, Benjamin R., Sweeney, Alex Year: 2017 The self-organizing maps (SOMs) approach is demonstrated as a way to identify a range of archetypal large-scale meteorological patterns (LSMPs) over the northwestern United States and connect these patterns with local-scale temperature and precipitation extremes. SOMs are used to construct a set of 12 characteristic LSMPs (nodes) based on daily reanalysis circulation fields spanning the range of observed synoptic-scale variability for the summer and winter seasons for the period 1979–2013. Composites of surface variables are constructed for subsets of days assigned to each node to explore relationships between temperature, precipitation, and the node patterns. The SOMs approach also captures interannual variability in daily weather regime frequency related to El Niño–Southern Oscillation. Temperature and precipitation extremes in high-resolution gridded observations and in situ station data show robust relationships with particular nodes in many cases, supporting the approach as a way to identify LSMPs associated with local extremes. Assigning days from the extreme warm summer of 2015 and wet winter of 2016 to nodes illustrates how SOMs may be used to assess future changes in extremes. These results point to the applicability of SOMs to climate model evaluation and assessment of future projections of local-scale extremes without requiring simulations to reliably resolve extremes at high spatial scales. Close