Abstract

A Monte Carlo framework is adopted for propagating uncertainty in dynamically downscaled seasonal forecasts of area-averaged daily precipitation to associated streamflow response calculations. Daily precipitation is modeled as a mixture of two stochastic processes: a binary occurrence process and a continuous intensity process, both exhibiting serial correlation. The parameters of these processes (e.g., the proportion of wet days and the average wet-day precipitation intensity in a month) are derived from the forecast record. Parameter uncertainty is characterized via an empirical Bayesian model, whereby such parameters are modeled as random with a specific joint probability distribution. The hyperparameters specifying this probability distribution are derived from historical precipitation records at the study basin. Simulated parameter values are then generated using the Bayesian model, leading to alternative synthetic daily precipitation records simulated via the stochastic precipitation model. The set of such synthetic precipitation records is finally input to a physically based deterministic hydrologic model for propagating uncertainty in forecasted precipitation to hydrologic impact assessment studies.

The stochastic simulation approach is applied for generating an ensemble (set) of synthetic area-averaged daily precipitation records at the Hopland basin in the northern California Coast Range for the winter months (December through February: DJF) of 1997/98. The parameters of the stochastic precipitation model are derived from a seasonal precipitation forecast based on the Regional Climate System Model (RCSM), available at a 36-km² grid spacing. The large-scale forcing input to RCSM for dynamical downscaling was a seasonal prediction of the University of California, Los Angeles, Atmospheric General Circulation Model. A semidistributed deterministic hydrologic model (“TOPMODEL”) is then used for calculating the streamflow response for each member of the area-averaged precipitation ensemble set. Uncertainty in the parameters of the stochastic precipitation model is finally propagated to associated streamflow response, by considering parameter values derived from historical (DJF 1958–92) area-averaged precipitation records at Hopland.

Abstract

Regional-scale projections of climate change signals due to increases in atmospheric CO₂ are generated for the western United States using a regional climate model (RCM) nested within two global scenarios from a GCM. The downscaled control climate improved the local accuracy of the GCM results substantially. The downscaled control climate is reasonably close to the results of an 8-yr regional climate hindcast using the same RCM nested within the NCEP–NCAR reanalysis, despite wet biases in high-elevation regions along the Pacific coast.

The downscaled near-surface temperature signal ranges from 3 to 5 K in the western United States. The projected warming signals generally increase with increasing elevation, consistent with earlier studies for the Swiss Alps and the northwestern United States. In addition to the snow–albedo feedback, seasonal variations of the low-level flow and soil moisture appear to play important roles in the spatial pattern of warming signals. Projected changes in precipitation characteristics are mainly associated with increased moisture fluxes from the Pacific Ocean and the increase in elevation of freezing levels during the cold season. Projected cold season precipitation increases substantially in mountainous areas along the Pacific Ocean. Most of the projected precipitation increase over the Sierra Nevada and the Cascades is in rainfall, while snowfall generally decreases except above 2500 m. Projected changes in summer rainfall are small. The snow budget signals are characterized by decreased (increased) cold season snowfall (snowmelt) and reduced snowmelt during spring and summer. The projected cold season runoff from high-elevation regions increases substantially in response to increased cold season rainfall and snowmelt, while the spring runoff decreases due to an earlier depletion of snow, except above 2500 m.

Abstract

A seasonal prediction model of tropical cyclone (TC) activities for the period August–October over the North Atlantic (NA) has been developed on the basis of TC track patterns. Using the fuzzy c-means method, a total of 432 TCs in the period 1965–2012 are categorized into the following four groups: 1) TCs off the U.S. East Coast, 2) TCs over the Gulf of Mexico, 3) TCs that recurve into the open ocean of the central NA, and 4) TCs that move westward in the southern NA. The model is applied to predict the four TC groups separately in conjunction with global climate forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2). By adding the distributions of the four TC tracks with precalculated weighting factors, this seasonal TC forecast model provides the spatial distribution of TC activities over the entire NA basin. Multiple forecasts initialized in six consecutive months from February to July are generated at monthly intervals to examine the applicability of this model in operational TC forecasting. Cross validations of individual forecasts show that the model can reasonably predict the observed TC frequencies over NA at the 99% confidence level. The model shows a stable spatial prediction skill, proving its advantage for forecasting regional TC activities several months in advance. In particular, the model can generate reliable information on regional TC counts in the near-coastal regions as well as in the entire NA basin.

Abstract

Despite tremendous advancements in dynamical models for weather forecasting, statistical models continue to offer various possibilities for tropical cyclone (TC) track forecasting. Herein, a track-pattern-based approach was developed to predict a TC track for a lead time of 6–8 days over the western North Pacific (WNP), utilizing historical tracks in conjunction with dynamical forecasts. It is composed of four main steps: 1) clustering historical tracks similar to that of an operational 5-day forecast in their early phase into track patterns, and calculating the daily mean environmental fields (500-hPa geopotential height and steering flow) associated with each track; 2) deriving the two environmental variables forecasted by dynamical models; 3) evaluating pattern correlation coefficients between the two environmental fields from step 1 and those from dynamical model for a lead times of 6–8 days; and 4) producing the final track forecast based on relative frequency maps obtained from the historical tracks in step 1 and the pattern correlation coefficients obtained from step 3. TCs that formed in the WNP and lasted for at least 7 days, during the 9-yr period 2011–19 were selected to verify the resulting track-pattern-based forecasts. In addition to the performance comparable to dynamical models under certain conditions, the track-pattern-based model is inexpensive, and can consistently produce forecasts over large latitudinal or longitudinal ranges. Machine learning techniques can be implemented to incorporate nonlinearity in the present model for improving medium-range track forecasts.

Abstract

The authors investigate the short-term relationship between aerosol concentrations and summer rainfall frequency in China using the daily surface observations of particulate matters with a diameter of less than 10 μm (PM10) mass concentration, rainfall, and satellite-observed cloud properties. Results in this study reveal that on the time scale of a few days aerosol concentration is positively correlated with the frequency of moderate-rainfall (10–20 mm day⁻¹) days but is negatively correlated with the frequency of light-rainfall (<5 mm day⁻¹) days. Satellite observations of cloud properties show that higher aerosol concentrations are positively correlated with the increase in mixed cloud amount, cloud effective radius, cloud optical depth, and cloud-top heights; this corresponds to the decrease in low-level liquid clouds and the increase in midlevel ice–mixed clouds. Based on this analysis, the authors hypothesize that the increase in aerosol concentration results in the increase in summer rainfall frequency in China via enhanced ice nucleation in the midtroposphere. However, over the past few decades, observations show an increasing long-term trend in aerosol concentration but decreasing trends in summer rainfall frequency and relative humidity (RH) in China. Despite the short-term positive relationship between summer rainfall frequency and aerosol concentration found in this study, the long-term variations in summer rainfall frequency in China are mainly determined by other factors including RH variation possibly caused by global and regional climate changes. A continuous decrease in RH resulting in less summer rainfall frequency may further enhance aerosol concentrations in the future in conjunction with the increase in the anthropogenic emissions.

Abstract

This study examines the impacts of global warming on the timing of plant habitat changes in the twenty-first century using climate scenarios from multiple global climate models (GCMs). The plant habitat changes are predicted by driving the bioclimate rule in a dynamic global vegetation model using the climate projections from 16 coupled GCMs. The timing of plant habitat changes is estimated by the first occurrence of specified fractional changes (10%, 20%, and 30%). All future projections are categorized into three groups by the magnitude of the projected global-mean land surface temperature changes: low (<2.5 K), medium (2.5–3.5 K), and high (>3.5 K) warming. During the course of the twenty-first century, dominant plant habitat changes are projected in ecologically transitional (i.e., from tropical to temperate and temperate to boreal) regions. The timing of plant habitat changes varies substantially according to regions. In the low-warming group, habitat changes of 10% in southern Africa occur in 2028, earlier than in the Americas by more than 70 yr. Differences in the timing between regions increase with the increase in warming and fractional threshold. In the subtropics, fast plant habitat changes are projected for the Asia and Africa regions, where countries of relatively small gross domestic product (GDP) per capita are concentrated. Ecosystems in these regions will be more vulnerable to global warming, because countries of low economic power lack the capability to deal with the warming-induced habitat changes. Thus, it is important to establish international collaboration via which developed countries provide assistance to mitigate the impacts of global warming.

Abstract

Prediction of tropical cyclone (TC) activity is essential to better prepare for and mitigate TC-induced disasters. Although many studies have attempted to predict TC activity on various time scales, very few have focused on near-future predictions. Here a decrease in seasonal TC activity over the North Atlantic (NA) for 2016–30 is shown using a track-pattern-based TC prediction model. The TC model is forced by long-term coupled simulations initialized using reanalysis data. Unfavorable conditions for TC development including strengthened vertical wind shear, enhanced low-level anticyclonic flow, and cooled sea surface temperature (SST) over the tropical NA are found in the simulations. Most of the environmental changes are attributable to cooling of the NA basinwide SST (NASST) and more frequent El Niño episodes in the near future. The consistent NASST warming trend in the projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) suggests that natural variability is more dominant than anthropogenic forcing over the NA in the near-future period.

Abstract

The authors present a seasonal hindcast and prediction of precipitation in the western United States and stream flow in a northern California coastal basin for December 1997–February 1998 (DJF) using the Regional Climate System Model (RCSM). In the seasonal hindcast simulation, in which the twice-daily National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis was used for the initial conditions and time-dependent boundary forcing, RCSM has simulated realistically the temporal and spatial variations of precipitation in California and stream flow in a northern California coastal basin. For the headwater basin of the Russian River in the northern California Coast Ranges, the Topography-Based Hydrologic Model (TOPMODEL) forced by observed daily precipitation resulted in a correlation coefficient of 0.88 between observed and simulated DJF stream flow. In the coupled stream flow hindcast, the authors obtained a correlation coefficient of 0.7 between simulated and observed stream flow for the same period. The coupled hindcast has generally overestimated (underestimated) low (high) flow events in the basin. Errors in the simulated stream flow were due mostly to the errors in the simulated precipitation. A seasonal hydroclimate prediction experiment, in which RCSM was nested within the global forecast data from the University of California, Los Angeles, GCM, has predicted well the season-total precipitation in the western United States. Temporal variations of predicted precipitation were affected strongly by the predictability of the general circulation model. The predicted DJF-total snowfall agrees well with the snowfall simulated in the hindcast, especially in the central Cascades and the Sierra Nevada, where snowfall was the heaviest.

Abstract

A numerical study of precipitation and river flow from November 1994 to May 1995 at two California basins is presented. The Hopland watershed of the Russian River in the northern California Coastal Range and the headwater of the North Fork American River in the northern Sierra Nevada were selected to investigate the hydroclimate, snow budget, and streamflow at different elevations. Simulated precipitation and streamflow at the Hopland basin closely approximated observed values. An intercomparison between the semidistributed TOPMODEL and two versions of the lumped Sacramento model for the severe storm event of January 1995 indicates that both types of models predicted a similar response of river outflows from this basin, with the exception that TOPMODEL predicted a faster recession of river flow with less base flow after precipitation ended. Precipitation in this low-elevation watershed was predominantly in the form of rain, causing a fast streamflow response. The high-elevation Sierra Nevada watershed received most of its precipitation as snowfall. As a result, the frozen water held in surface storage delayed runoff and streamflow. Application of a simple elevation-dependent snowfall and rainfall partitioning scheme showed the significance of finescale terrain variation in the surface hydrology at high-elevation watersheds.

Abstract

Atmospheric rivers (ARs) are long and narrow regions of strong horizontal water vapor transport. Upon landfall, ARs are typically associated with heavy precipitation and strong surface winds. A quantitative understanding of the atmospheric conditions that favor extreme surface winds during ARs has implications for anticipating and managing various impacts associated with these potentially hazardous events. Here, a global AR database (1999–2014) with relevant information from MERRA-2 reanalysis, QuikSCAT, and AIRS satellite observations is used to better understand and quantify the role of near-surface static stability in modulating surface winds during landfalling ARs. The temperature difference between the surface and 1 km MSL (ΔT; used here as a proxy for near-surface static stability), along with integrated water vapor transport (IVT), is analyzed to quantify their relationships to surface winds using bivariate linear regression. In four regions where AR landfalls are common, the MERRA-2-based results indicate that IVT accounts for 22%–38% of the variance in surface wind speed. Combining ΔT with IVT increases the explained variance to 36%–52%. Substitution of QuikSCAT surface winds and AIRS ΔT in place of the MERRA-2 data largely preserves this relationship (e.g., 44% as compared with 52% explained variance for U.S. West Coast). Use of an alternate static stability measure—the bulk Richardson number—yields a similar explained variance (47%). Last, AR cases within the top and bottom 25% of near-surface static stability indicate that extreme surface winds (gale or higher) are more likely to occur in unstable conditions (5.3% and 14.7% during weak and strong IVT, respectively) than in stable conditions (0.58% and 6.15%).