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Of concern to Colorado River management, as operating guidelines post-2026 are being considered, is whether water resource recovery from low flows during 2000–20 is possible. Here, we analyze new simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6) to determine plausible climate impacts on Colorado River flows for 2026–50 when revised guidelines would operate. We constrain projected flows for Lees Ferry, the gauge through which 85% of the river flow passes, using its estimated sensitivity to meteorological variability together with CMIP6-projected precipitation and temperature changes. The critical importance of precipitation, especially its natural variability, is emphasized. Model projections indicate increased precipitation in the upper Colorado River basin due to climate change, which alone increases river flows by 5%–7% (relative to a 2000–20 climatology). Depending on the river's temperature sensitivity, this wet signal compensates for some, if not all, of the depleting effects of basin warming. Considerable internal decadal precipitation variability (∼5% of the climatological mean) is demonstrated, driving a greater range of plausible Colorado River flow changes for 2026–50 than previously surmised from treatment of temperature impacts alone: the overall precipitation-induced Lees Ferry flow changes span from −25% to +40%, contrasting with a range from −30% to −5% from expected warming effects only. Consequently, extreme low and high flows are more likely. Lees Ferry flow projections, conditioned on initial drought states akin to 2000–20, reveal substantial recovery odds for water resources, albeit with elevated risks of even further flow declines than in recent decades. Significance Statement: Increasing temperatures have led to concerns that Colorado River flows will be permanently reduced due to global warming. Here, we analyze precipitation, surface temperature, and streamflow (at Lees Ferry) in the upper Colorado River basin since 1895 revealing precipitation to be the largest contributor to runoff variability while temperature variability has been a much smaller contributor. New climate model projections indicate increased precipitation in the upper basin due to global warming, acting to increase river flows by 5%–7% during 2026–50. This wet signal compensates for some of the depleting effects from further basinwide warming. Critically, the large intrinsic variability in precipitation is demonstrated to yield a wider range of future Colorado River flows than previously foreseen based solely on considering temperature effects. [ABSTRACT FROM AUTHOR]

A familiar saying among agricultural producers of the U.S. northern Great Plains region comprising Montana, the Dakotas, and the adjacent Canadian Prairies is, 'We're always in a drought. It just [...]

INTRODUCTION. Drought conditions developed rapidly during the spring and summer of 2017 over the northern Great Plains states of South Dakota, North Dakota, and Montana (Fig. la). On 2 May [...]

We diagnose physical factors related to frequent compound drought and heat extremes over a Middle East and Southwest Asia (MESA; 30°–40°N, 35°–65°E) region in a recent (1999–2022) compared to a prior (1951–98) period. The recent compound extremes were related to conflict, disease transmission, and water shortages in this already semiarid region. Observed estimates and four transient climate model ensembles are used to identify the effect of El Niño–Southern Oscillation (ENSO) and atmospheric forcing by greenhouse gases and aerosols on these compound extremes in autumn (September–November), winter (December–February), spring (March–May), and summer (June–August) that may lead to practical forecast skill for future compound events. Observations and climate models indicate that MESA compound drought and heat in the autumn, winter, and spring wet seasons for the recent period were related to the La Niña phase of ENSO and an attendant northward shift of the storm track that hinders precipitation-bearing storms from moving through MESA. A comparison of different conditions in the model simulations is used to isolate the effects of La Niña and the atmospheric forcing by greenhouse gases and aerosols on compound MESA drought and heat. A comparison of recent and prior periods in the climate models, which isolates the effects of the atmospheric forcing, indicates that greenhouse gases and aerosols are related to the increases in MESA heat frequency in all seasons. A comparison of La Niña to ENSO neutral and El Niño in the recent period of the climate models indicates that La Niña is related to increases in MESA drought frequency in the wet seasons. Significance Statement: Compound drought and heat pose serious threats to the Middle East and Southwest Asia (MESA) where political and socioeconomic challenges leave its people vulnerable to climate extremes. In this region, frequent seasonal compound drought and heat in a recent (1999–2022) compared to a prior (1951–98) period were related to conflict and water shortages. Physical factors related to these compound extremes in the recent period over MESA were identified, potentially rendering future occurrences predictable. La Niña and atmospheric forcing by greenhouse gases and aerosols contributed to the compound extremes, with the former related to anomalously low precipitation in the September–November, December–February, and March–May wet seasons and the latter related to anomalous high temperatures in all seasons, including June–August. [ABSTRACT FROM AUTHOR]

Introduction. This is a story of two extreme events-one that was expected but failed to occur and the other that actually did occur but was not anticipated. The one that [...]

Model simulations indicate that anthropogenic climate change has made extreme snowstorms less likely over the mid-Atlantic United States. Empirical evidence shows no decline since 1901, with recent storms colder than [...]

We examine how physical factors spanning climate and weather contributed to record warmth over the central and eastern United States in March 2012, when daily temperature anomalies at many locations exceeded 20°C. Over this region, approximately 1°C warming in March temperatures has occurred since 1901. This long-term regional warming is an order of magnitude smaller than temperature anomalies observed during the event, indicating that most of the extreme warmth must be explained by other factors. Several lines of evidence strongly implicate natural variations as the primary cause for the extreme event. The 2012 temperature anomalies had a close analog in an exceptionally warm U.S. March occurring over 100 years earlier, providing observational evidence that an extreme event similar to March 2012 could be produced through natural variability alone. Coupled model forecasts and simulations forced by observed sea surface temperatures (SSTs) show that forcing from anomalous SSTs increased the probability of extreme warm temperatures in March 2012 above that anticipated from the long-term warming trend. In addition, forcing associated with a strong Madden-Julian oscillation further increased the probability for extreme U.S. warmth and provided important additional predictive information on the timing and spatial pattern of temperature anomalies. The results indicate that the superposition of a strong natural variation similar to March 1910 on long-term warming of the magnitude observed would be sufficient to account for the record warm March 2012 U.S. temperatures. We conclude that the extreme warmth over the central and eastern United States in March 2012 resulted primarily from natural climate and weather variability--a substantial fraction of which was predictable., Record warm temperatures over the United States in March 2012 were mostly due to natural climate and weather variability. Nature's exuberant smashing of high temperature records in March 2012 can [...]

Inhomogeneity in gridded meteorological data may arise from the inclusion of inhomogeneous station data or from aspects of the gridding procedure itself. However, the homogeneity of gridded datasets is rarely questioned, even though an analysis of trends or variability that uses inhomogeneous data could be misleading or even erroneous. Three gridded precipitation datasets that have been used in studies of the Upper Colorado River basin were tested for homogeneity in this study: that of Maurer et al., that of Beyene and Lettenmaier, and the Parameter--Elevation Regressions on Independent Slopes Model (PRISM) dataset of Daly et al. Four absolute homogeneity tests were applied to annual precipitation amounts on a grid cell and on a hydrologic subregion spatial scale for the periods 1950-99 and 1916-2006. The analysis detects breakpoints in 1977 and 1978 at many locations in all three datasets that may be due to an anomalously rapid shift in the Pacific decadal oscillation. One dataset showed breakpoints in the 1940s that might be due to the widespread change in the number of available observing stations used as input for that dataset. The results also indicated that the time series from the three datasets are sufficiently homogeneous for variability analysis during the 1950-99 period when aggregated on a subregional scale. DOI: 10.1175/2010JAMC2484.1

This paper presents and evaluates a method for the construction of long-range and wide-area temporal spatial datasets of daily precipitation and temperature (maximum and minimum). This method combines the interpolation of daily ratios/fractions derived from ground-based meteorological station records and respective fields of monthly estimates. Data sources for the described implementation over the conterminous United States (CONUS) are two independent and quality-controlled inputs: 1) an enhanced compilation of daily observations derived from the National Climatic Data Center digital archives and 2) the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) maps. The results of this study show that this nonconventional interpolation preserves the spatial and temporal distribution of both the PRISM maps (monthly, topography-sensitive patterns) and the original daily observations. Statistics of a preliminary point comparison with the observed values at high-quality and independent reference sites show a reasonable agreement and a noticeable improvement over the nearest station method in orograpbically sensitive areas. The implemented datasets provide daily precipitation and temperature values at 2.5-min (around 4 km) resolution for 1960-2001. Combining seamless spatial and temporal coverage and topographic sensitivity characteristics, the datasets offer the potential for supporting current and future regional and historical hydrologic assessments over the CONUS.

One of the major concerns with detecting global climate change is the quality of the data. Climate data are extremely sensitive to errant values and outliers. Prior to analysis of these time series, it is important to remove outliers in a methodical manner. This study provides statistically derived bounds for the uncertainty associated with surface temperature and precipitation measurements and yields a baseline dataset for validation of climate models as well as for a variety of other climatological uses. A two-step procedure using objective analysis was used to identify outliers. The first step was a temporal check that determines if a particular monthly value is consistent with other monthly values for the same station. The second step utilizes six different spatial interpolation techniques to estimate each monthly time series. Each of the methods is ranked according to its respective correlation coefficients with the actual time series, and the technique with the highest correlation coefficient is chosen as the best estimator. For both temperature and precipitation, a multiple regression scheme was found to be the best estimator for the majority of records. Results from the two steps are merged, and a combined set of quality control flags are generated.