Projected twenty-first-century changes in the Central American mid-summer drought using statistically downscaled climate projections

2017 ◽  
Vol 17 (8) ◽  
pp. 2421-2432 ◽  
Author(s):  
Edwin P. Maurer ◽  
Nicholas Roby ◽  
Iris T. Stewart-Frey ◽  
Christopher M. Bacon
2019 ◽  
Vol 172 ◽  
pp. 69-87 ◽  
Author(s):  
Gil Lemos ◽  
Alvaro Semedo ◽  
Mikhail Dobrynin ◽  
Arno Behrens ◽  
Joanna Staneva ◽  
...  

2014 ◽  
Vol 27 (23) ◽  
pp. 8793-8808 ◽  
Author(s):  
Paul J. Northrop ◽  
Richard E. Chandler

Abstract A simple statistical model is used to partition uncertainty from different sources, in projections of future climate from multimodel ensembles. Three major sources of uncertainty are considered: the choice of climate model, the choice of emissions scenario, and the internal variability of the modeled climate system. The relative contributions of these sources are quantified for mid- and late-twenty-first-century climate projections, using data from 23 coupled atmosphere–ocean general circulation models obtained from phase 3 of the Coupled Model Intercomparison Project (CMIP3). Similar investigations have been carried out recently by other authors but within a statistical framework for which the unbalanced nature of the data and the small number (three) of scenarios involved are potentially problematic. Here, a Bayesian analysis is used to overcome these difficulties. Global and regional analyses of surface air temperature and precipitation are performed. It is found that the relative contributions to uncertainty depend on the climate variable considered, as well as the region and time horizon. As expected, the uncertainty due to the choice of emissions scenario becomes more important toward the end of the twenty-first century. However, for midcentury temperature, model internal variability makes a large contribution in high-latitude regions. For midcentury precipitation, model internal variability is even more important and this persists in some regions into the late century. Implications for the design of climate model experiments are discussed.


2015 ◽  
Vol 29 (1) ◽  
pp. 91-110 ◽  
Author(s):  
Fengpeng Sun ◽  
Alex Hall ◽  
Marla Schwartz ◽  
Daniel B. Walton ◽  
Neil Berg

Abstract Future snowfall and snowpack changes over the mountains of Southern California are projected using a new hybrid dynamical–statistical framework. Output from all general circulation models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive is downscaled to 2-km resolution over the region. Variables pertaining to snow are analyzed for the middle (2041–60) and end (2081–2100) of the twenty-first century under two representative concentration pathway (RCP) scenarios: RCP8.5 (business as usual) and RCP2.6 (mitigation). These four sets of projections are compared with a baseline reconstruction of climate from 1981 to 2000. For both future time slices and scenarios, ensemble-mean total winter snowfall loss is widespread. By the mid-twenty-first century under RCP8.5, ensemble-mean winter snowfall is about 70% of baseline, whereas the corresponding value for RCP2.6 is somewhat higher (about 80% of baseline). By the end of the century, however, the two scenarios diverge significantly. Under RCP8.5, snowfall sees a dramatic further decline; 2081–2100 totals are only about half of baseline totals. Under RCP2.6, only a negligible further reduction from midcentury snowfall totals is seen. Because of the spread in the GCM climate projections, these figures are all associated with large intermodel uncertainty. Snowpack on the ground, as represented by 1 April snow water equivalent is also assessed. Because of enhanced snowmelt, the loss seen in snowpack is generally 50% greater than that seen in winter snowfall. By midcentury under RCP8.5, warming-accelerated spring snowmelt leads to snow-free dates that are about 1–3 weeks earlier than in the baseline period.


2012 ◽  
Vol 26 (21) ◽  
pp. 8269-8288 ◽  
Author(s):  
Alvaro Semedo ◽  
Ralf Weisse ◽  
Arno Behrens ◽  
Andreas Sterl ◽  
Lennart Bengtsson ◽  
...  

Abstract Wind-generated waves at the sea surface are of outstanding importance for both their practical relevance in many aspects, such as coastal erosion, protection, or safety of navigation, and for their scientific relevance in modifying fluxes at the air–sea interface. So far, long-term changes in ocean wave climate have been studied mostly from a regional perspective with global dynamical studies emerging only recently. Here a global wave climate study is presented, in which a global wave model [Wave Ocean Model (WAM)] is driven by atmospheric forcing from a global climate model (ECHAM5) for present-day and potential future climate conditions represented by the Intergovernmental Panel for Climate Change (IPCC) A1B emission scenario. It is found that changes in mean and extreme wave climate toward the end of the twenty-first century are small to moderate, with the largest signals being a poleward shift in the annual mean and extreme significant wave heights in the midlatitudes of both hemispheres, more pronounced in the Southern Hemisphere and most likely associated with a corresponding shift in midlatitude storm tracks. These changes are broadly consistent with results from the few studies available so far. The projected changes in the mean wave periods, associated with the changes in the wave climate in the middle to high latitudes, are also shown, revealing a moderate increase in the equatorial eastern side of the ocean basins. This study presents a step forward toward a larger ensemble of global wave climate projections required to better assess robustness and uncertainty of potential future wave climate change.


2014 ◽  
Vol 15 (4) ◽  
pp. 1404-1418 ◽  
Author(s):  
Seshadri Rajagopal ◽  
Francina Dominguez ◽  
Hoshin V. Gupta ◽  
Peter A. Troch ◽  
Christopher L. Castro

Abstract Water managers across the United States face the need to make informed policy decisions regarding long-term impacts of climate change on water resources. To provide a scientifically informed basis for this, the evolution of important components of the basin-scale water balance through the end of the twenty-first century is estimated. Bias-corrected and spatially downscaled climate projections, from phase 3 of the Coupled Model Intercomparison Project (CMIP3) of the World Climate Research Programme, were used to drive a spatially distributed Variable Infiltration Capacity (VIC) model of hydrologic processes in the Salt–Verde basin in the southwestern United States. From the suite of CMIP3 models, the authors select a five-model subset, including three that best reproduce the historical climatology for the study region, plus two others to represent wetter and drier than model average conditions, so as to represent the range of GCM prediction uncertainty. For each GCM, data for three emission scenarios (A1B, A2, and B1) were used to drive the hydrologic model into the future. The projections of this model ensemble indicate a statistically significant 25% decrease in streamflow by the end of the twenty-first century. The primary cause for this change is due to projected decreases in winter precipitation accompanied by significant (temperature driven) reductions in storage of snow and increased winter evaporation. The results show that water management in central Arizona is highly likely to be impacted by changes in regional climate.


2008 ◽  
Vol 21 (11) ◽  
pp. 2651-2663 ◽  
Author(s):  
R. Knutti ◽  
M. R. Allen ◽  
P. Friedlingstein ◽  
J. M. Gregory ◽  
G. C. Hegerl ◽  
...  

Abstract Quantification of the uncertainties in future climate projections is crucial for the implementation of climate policies. Here a review of projections of global temperature change over the twenty-first century is provided for the six illustrative emission scenarios from the Special Report on Emissions Scenarios (SRES) that assume no policy intervention, based on the latest generation of coupled general circulation models, climate models of intermediate complexity, and simple models, and uncertainty ranges and probabilistic projections from various published methods and models are assessed. Despite substantial improvements in climate models, projections for given scenarios on average have not changed much in recent years. Recent progress has, however, increased the confidence in uncertainty estimates and now allows a better separation of the uncertainties introduced by scenarios, physical feedbacks, carbon cycle, and structural uncertainty. Projection uncertainties are now constrained by observations and therefore consistent with past observed trends and patterns. Future trends in global temperature resulting from anthropogenic forcing over the next few decades are found to be comparably well constrained. Uncertainties for projections on the century time scale, when accounting for structural and feedback uncertainties, are larger than captured in single models or methods. This is due to differences in the models, the sources of uncertainty taken into account, the type of observational constraints used, and the statistical assumptions made. It is shown that as an approximation, the relative uncertainty range for projected warming in 2100 is the same for all scenarios. Inclusion of uncertainties in carbon cycle–climate feedbacks extends the upper bound of the uncertainty range by more than the lower bound.


2009 ◽  
Vol 22 (16) ◽  
pp. 4261-4280 ◽  
Author(s):  
Oliver Timm ◽  
Henry F. Diaz

Abstract A linear statistical downscaling technique is applied to the projection of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) climate change scenarios onto Hawaiian rainfall for the late twenty-first century. Hawaii’s regional rainfall is largely controlled by the strength of the trade winds. During the winter months, disturbances in the westerlies can produce heavy rainfall throughout the islands. A diagnostic analysis of sea level pressure (SLP), near-surface winds, and rainfall measurements at 134 weather observing stations around the islands characterize the correlations between the circulation and rainfall during the nominal wet season (November–April) and dry season (May–October). A comparison of the base climate twentieth-century AR4 model simulations with reanalysis data for the period 1970–2000 is used to define objective selection criterion for the AR4 models. Six out of 21 available models were chosen for the statistical downscaling. These were chosen on the basis of their ability to more realistically simulate the modern large-scale circulation fields in the Hawaiian Islands region. For the AR4 A1B emission scenario, the six analyzed models show important changes in the wind fields around Hawaii by the late twenty-first century. Two models clearly indicate opposite signs in the anomalies. One model projects 20%–30% rainfall increase over the islands; the other model suggests a rainfall decrease of about 10%–20% during the wet season. It is concluded from the six-model ensemble that the most likely scenario for Hawaii is a 5%–10% reduction of the wet-season precipitation and a 5% increase during the dry season, as a result of changes in the wind field. The authors discuss the sources of uncertainties in the projected rainfall changes and consider future improvements of the statistical downscaling work and implications for dynamical downscaling methods.


2019 ◽  
Author(s):  
Stella Todzo ◽  
Adeline Bichet ◽  
Arona Diedhiou

Abstract. This study uses the high resolution outputs of the recent CORDEX-AFRICA climate projections to investigate the future changes in different aspects of the hydrological cycle over West Africa. Over the twenty-first century, temperatures in West Africa are expected to increase at a faster rate (+ 0.5 °C per decade) than the global average (+ 0.3 °C per decade), and mean precipitation is expected to increase over the Guinea Coast (+ 0.03 mm/day per decade) but decrease over the Sahel (− 0.005 mm/day per decade). In addition, precipitation is expected to become more intense (+ 0.2 mm/day per decade) and less frequent (− 1.5 days per decade) over the entire West Africa as a results of increasing regional temperature (precipitation intensity increases on average by + 0.35 mm/day per °C and precipitation frequency decreases on average by − 2.2 days per °C). Over the Sahel, the average length of dry spells is also expected to increase with temperature (+ 4 % days per °C), which increases the likelihood for droughts with warming in this sub-region. Hence, the hydrological cycle is expected to increase throughout the twenty-first century over the entire West Africa, on average by + 11 % per °C over the Sahel as a result of increasing precipitation intensity and lengthening of dry spells, and on average by + 3 % per °C over the Guinea Coast as a result of increasing precipitation intensity only.


2016 ◽  
Vol 55 (10) ◽  
pp. 2301-2322 ◽  
Author(s):  
D. J. Rasmussen ◽  
Malte Meinshausen ◽  
Robert E. Kopp

AbstractQuantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, two such methods, surrogate/model mixed ensemble (SMME) and Monte Carlo pattern/residual (MCPR), are developed and then are applied to construct joint probability density functions (PDFs) of temperature and precipitation change over the twenty-first century for every county in the United States. Both methods produce likely (67% probability) temperature and precipitation projections that are consistent with the Intergovernmental Panel on Climate Change’s interpretation of an equal-weighted Coupled Model Intercomparison Project phase 5 (CMIP5) ensemble but also provide full PDFs that include tail estimates. For example, both methods indicate that, under “Representative Concentration Pathway” 8.5, there is a 5% chance that the contiguous United States could warm by at least 8°C between 1981–2010 and 2080–99. Variance decomposition of SMME and MCPR projections indicates that background variability dominates uncertainty in the early twenty-first century whereas forcing-driven changes emerge in the second half of the twenty-first century. By separating CMIP5 projections into unforced and forced components using linear regression, these methods generate estimates of unforced variability from existing CMIP5 projections without requiring the computationally expensive use of multiple realizations of a single GCM.


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