Carlos M. Carrillo

Can a regional climate model improve the ability to forecast the North American monsoon

AbstractGlobal climate models are challenged to represent the North American monsoon, in terms of its climatology and interannual variability. To investigate whether a regional atmospheric model can improve warm season forecasts in North America, a retrospective Climate Forecast System (CFS) model reforecast (1982–2000) and the corresponding NCEP–NCAR reanalysis are dynamically downscaled with the Weather Research and Forecasting model (WRF), with similar parameterization options as used for high-resolution numerical weather prediction and a new spectral nudging capability. The regional model improves the climatological representation of monsoon precipitation because of its more realistic representation of the diurnal cycle of convection. However, it is challenged to capture organized, propagating convection at a distance from terrain, regardless of the boundary forcing data used. Dynamical downscaling of CFS generally yields modest improvement in surface temperature and precipitation anomaly correlations...

A Robust Null Hypothesis for the Potential Causes of Megadrought in Western North America

AbstractThe western United States was affected by several megadroughts during the last 1200 years, most prominently during the Medieval Climate Anomaly (MCA; 800 to 1300 CE). A null hypothesis is developed to test the possibility that, given a sufficiently long period of time, these events are inevitable and occur purely as a consequence of internal climate variability. The null distribution of this hypothesis is populated by a linear inverse model (LIM) constructed from global sea surface temperature anomalies and self-calibrated Palmer drought severity index data for North America. Despite being trained only on seasonal data from the late twentieth century, the LIM produces megadroughts that are comparable in their duration, spatial scale, and magnitude to the most severe events of the last 12 centuries. The null hypothesis therefore cannot be rejected with much confidence when considering these features of megadrought, meaning that similar events are possible today, even without any changes to boundary...

The more extreme nature of U.S. warm season climate in the recent observational record and two “well‐performing” dynamically downscaled CMIP3 models

Arid and semiarid regions located in subtropical zones are projected to experience the most adverse impacts of climate change. During the warm season, observations and Intergovernmental Panel on Climate Change global climate models generally support a “wet gets wetter, dry gets drier” hypothesis in these regions, which acts to amplify the climatological transitions in the context of the annual cycle. In this study, we consider changes in U.S. early warm season precipitation in the observational record and regional climate model simulations driven by two “well-performing” dynamically downscaled Coupled Model Intercomparison Project phase 3 (CMIP3) models (Hadley Centre Coupled Model, version 3 and Max Planck Institute (MPI) European Centre/Hamburg Model 5) that have a robust climatological representation of the North American Monsoon System (NAMS). Both observations and model results show amplification in historical seasonal transitions of temperature and precipitation associated with NAMS development, with Weather Research and Forecasting (WRF)-MPI better representing the observed signal. Assuming the influence of remote Pacific sea surface temperature (SST) forcing associated with the El Nino–Southern Oscillation and Pacific Decadal Variability (ENSO-PDV) on U.S. regional climate remains the same in the 21st century, similar extreme trends are also projected by WRF-MPI for the next 30 years. A methodology is also developed to objectively analyze how climate change may be synergistically interacting with ENSO-PDV variability during the early warm season. Our analysis suggests that interannual variability of warm season temperature and precipitation associated with Pacific SST forcing is becoming more extreme, and the signal is stronger in the observed record.

Objective Climatological Analysis of Extreme Weather Events in Arizona during the North American Monsoon

Strategic Environmental Research and Development Program (SERDP) through the U.S. Departments of Defense and Energy [RC-2205]; U.S. Environmental Protection Agency; Universidad Nacional Autonoma de Mexico Programa de Apoyo a Proyectos de Investigation e Innovation Tecnologica (UNAM PAPIIT) [IA100916]

Measuring park visitation vulnerability to climate extremes in U.S. Rockies National Parks tourism

ABSTRACT Changes in temperature and precipitation can affect tourist experiences. This study examines how summer park visitation has changed in response to temperature and precipitation extremes. The study goals were two-fold. The first is to introduce a framework and the second is to test it in a pilot region with four mountainous National Parks. The framework is designed to compare the vulnerability of seasonal park visitation to shifts in a combined indicator of temperature and precipitation. It uniquely considers needed measurements, and the data required to conduct an analysis. The second goal is to test it in four destinations in the U.S. Northern Rockies, including Glacier, Yellowstone, Grand Teton, and Rocky Mountain National Parks. The preliminary test reveals outlier cases of visitation under wet and dry extremes. The analysis connects time series climate and visitation data for the peak summer season from 1991–2012. Outlier analysis illustrates more change in extremely dry conditions, with four out of the six dry-year outliers resulting in a visitation decline. Whether this decline in park tourism is attributable to climate features, economic factors, or conscious management decisions, these drops have significant economic impacts: estimates of changes in visitor spending during dry years are between roughly 9 and 90 million USD. These differences may be connected to the popular activities in each park, and the extent they are dependent on weather conditions. This framework can be used to test the relationship between climate and tourism visitation in other regions, in various seasons and time frames. The work may inform the tourist sector in adjusting and planning for a range of conditions. We discuss opportunities and conclude with additional needs for understanding the mechanisms behind risk in mountain park tourism under climate extremes.

Multi-year climate variability in the Southwestern United States within a context of a dynamically downscaled twentieth century reanalysis

This investigation evaluates whether there is coherency in warm and cool season precipitation at the low-frequency scale that may be responsible for multi-year droughts in the US Southwest. This low-frequency climate variability at the decadal scale and longer is studied within the context of a twentieth-century reanalysis (20CR) and its dynamically-downscaled version (DD-20CR). A spectral domain matrix methods technique (Multiple-Taper-Method Singular Value Decomposition) is applied to these datasets to identify statistically significant spatiotemporal precipitation patterns for the cool (November–April) and warm (July–August) seasons. The low-frequency variability in the 20CR is evaluated by exploring global to continental-scale spatiotemporal variability in moisture flux convergence (MFC) to the occurrence of multiyear droughts and pluvials in Central America, as this region has a demonstrated anti-phase relationship in low-frequency climate variability with northern Mexico and the southwestern US By using the MFC in lieu of precipitation, this study reveals that the 20CR is able to resolve well the low-frequency, multiyear climate variability. In the context of the DD-20CR, multiyear droughts and pluvials in the southwestern US (in the early twentieth century) are significantly related to this low-frequency climate variability. The precipitation anomalies at these low-frequency timescales are in phase between the cool and warm seasons, consistent with the concept of dual-season drought as has been suggested in tree ring studies.

Spring Onset Predictability in the North American Multimodel Ensemble

4 5 This study assesses the predictability of spring onset using an index of its interannual variability. 6 We use the North American Multi-Model Ensemble (NMME) experiment to assess this 7 predictability. The input dataset to compute spring onset index, SI-x, were treated with a daily 8 joint bias correction (JBC) approach, and the SI-x outputs were post-processed using an 9 ensemble model output statistic (EMOS) approach—non-homogeneous Gaussian regression. 10 This EMOS approach quantifies the effect of training period length and ensemble size on 11 forecast skill. The highest range of predictability for the timing spring onset is from 10 to 60 12 days, and it is located along a narrow band between 35° to 45°N in the US. Using rank 13 probability scores based on quantiles (q), a forecast threshold (q) of 0.5 provides a range of 14 predictability that falls into two categories 10-40 and 40-60 days, which seems to represent the 15 effect of the intra-seasonal scale. Using higher thresholds (q=0.6 and 0.7) predictability shows 16 lower range with values around 10-30 days. The post-processing work using JBC improves the 17 predictability skill by 13% from uncorrected results. Using EMOS, a significant positive change 18 in the skill score is noted in regions where the skill with JBC shows evidence of improvement. 19 The consensus of these techniques shows that regions of better predictability can be expanded. 20