2,070 research outputs found
Recommended from our members
A framework for the simulation of regional decadal variability for agricultural and other applications
Climate prediction on decadal time scales is currently an active area of research, and reliable model-based forecasts of regional "near-term" climate change have yet to be demonstrated. In the absence of such forecasts, synthetic data sequences that capture the statistical properties of observed near-term climate variability have potential value. Incorporation of a climate change component in such sequences can help define risk estimates for a range of climatic stresses, including those lying beyond what has been experienced in the past. Properly conditioned simulations can be used to drive agricultural, hydrological or other application models, enabling resilience testing of adaptation or decision systems. The use of statistically-based methods enables the efficient generation of large ensembles of synthetic sequences and consequently, the creation of well-defined probabilistic risk estimates. In this report we examine some procedures for the generation of synthetic climate sequences that incorporate both the statistics of observed variability and expectations regarding future regional climate change. Model fitting and simulation are considered in the framework of classical time series analysis, with methodology conditioned by requirements particular to the decadal climate problem. A method of downscaling annualized simulations to the daily time step, while preserving subannual statistical properties, is presented and other possible methods discussed. Deployment in the applications setting, the details of which may vary considerably, depending on regional climate characteristics, available data and the design of follow-on models, is considered and elements of a case study presented
Recommended from our members
An information-theoretic perspective on teleconnections
A classical paradigm for terrestrial climate variability involves remote sea surface temperature forcing, communicated to receptor regions via atmospheric teleconnections. Here the teleconnection link is abstracted in terms of Shannon's information-theoretic measure “channel capacity.” An upper bound on the channel capacity for December-January-February (DJF) seasonal precipitation teleconnections with sea surface temperature in the NINO3.4 region, when both variables are tercile-quantized, is estimated as 1 bit, meaning that it is only marginally possible to distinguish reliably between two NINO3.4 input states on the basis of observed precipitation output amounts, the central tercile acting principally to degrade reliability. A relationship between the channel capacity in a continuous model and the correlation coefficient is established; the corresponding nonlinear transformation provides a useful shift in perspective on the communication of information as such via teleconnections
Recommended from our members
Analysis of Indian monsoon daily rainfall on subseasonal to multidecadal time-scales using a hidden Markov model
A 70-year record of daily monsoon-season rainfall at a network of 13 stations in central western India is analyzed using a 4-state homogeneous hidden Markov model. The diagnosed states are seen to play distinct roles in the seasonal march of the monsoon, can be associated with 'active' and 'break' monsoon phases and capture the northward propagation of convective disturbances associated with the intraseasonal oscillation. Interannual variations in station rainfall are found to be associated with the alternation, from year to year, in the frequency of occurrence of wet and dry states; this mode of variability is well correlated with both all-India monsoon rainfall and an index characterizing the strength of the El Niño Southern Oscillation. Analysis of low-passed time series suggests that variations in state frequency are responsible for the modulation of monsoon rainfall on multidecadal time-scales as well
Recommended from our members
Review of Downscaling Methodologies for Africa Climate Applications
Downscaling is the term used to describe the various methods used to translate the climate projections from coarse resolution GCMs to finer resolutions deemed more useful for assessing impacts. Projections of future climate are produced using complex, coupled atmosphere-ocean models (GCMs). The GCMs are most reliable at the continental scale. Due to the inherent uncertainty of the climate system and the inevitable existence of model errors, multi-model ensembling is the recommended approach for characterizing expected climate changes. As downscaling is dependent on the ability of GCMs to successfully project the climate change signal, it is limited to where that signal is clear. Assessments of climate change in Africa indicate some consensus of reduced winter rainfall in southern Africa, increased annual rainfall in east Africa and uncertainty for the rest of Africa. Selection of GCMs that "do better" over Africa, or any region, is difficult and probably not warranted, given the general parity in model skill and the difficulty in identifying which models are more skillful. Ensemble means or medians offer the highest level of projection accuracy. Downscaling approaches are generally categorized as dynamical, using regional climate models, and statistical, using empirical relationships. However, dynamical downscaling often includes statistical modeling in the form of "bias correction." Dynamical downscaling is useful for incorporating topographic features, such as strong orography, and land use and vegetation. It is recommended where those features play a significant role in regional climate. However, computational time and the uncertainties that accompany complex models outweigh the benefits of dynamical downscaling where these features are not significant. The spatial resolution that can be achieved is on the order of tens of kilometers. Statistical downscaling is simpler and more efficient than dynamical downscaling. It is preferred where estimates of specific variables, especially at point locations, are sought for input to sector models (e.g., hydrologic models) or decision making. However, statistical modeling can mask a true understanding of regional climate dynamics and estimates may be overconfident. In summary, downscaling is best understood as an attempt to increase the understanding of climate change influences at the regional scale. In that context, a variety of methodologies should be explored, using all tools possible to increase that understanding. A set of "Best Practices" is recommended for pursuing this effort
Recommended from our members
Categorical representation of North American precipitation projections
We explore use of the familiar tercile framework of seasonal forecasting for the characterization of 21st-century precipitation projections over North America. Consistent with direct analyses of modeled precipitation change, in a superensemble of CMIP5 simulations an unambiguous pattern of shifted tercile population statistics develops as the globe warms. Expressed categorically, frequencies for the low (i.e., dry) tercile increase in the southwestern United States and southward into Mexico and decrease across the northern tier of North America, while counts for the high tercile shift in the opposite sense. We show that as the 21st-century proceeds, changes become statistically significant over wide regions in the pointwise sense and also when considered as projections on model-specific climate change “fingerprints”. Background noise in the superensemble, against which significance is established, comprises both structural model uncertainty and natural climate variability. The robustness of these findings makes a compelling case for long-range planning for a dryer future in the American Southwest and southward and wetter one to the north and especially northeast, while communication is facilitated by widespread user familiarity with the tercile format
Recommended from our members
Climate Variability and Change in Western U.S. Rangelands
We examine variability and change components of precipitation and minimum and maximum daily temperatures, and the derived variables potential evapotranspiration (PET) and the Palmer Drought Severity Index (PDSI), over rangelands in the region 30-50N, 100- 125W. We focus on areas administered by the U.S. Bureau of Land Management (BLM) and Bureau of Indian Affairs (BIA), with a view toward understanding how future climate variations may affect ecosystems, and ultimately, grazing on these lands. Based on an analysis of the annual precipitation cycle we adopt a three-season partition for the year, classifying land areas by season of maximum precipitation; this yields a coherent subregional map. Masking with a combined BLM/BIA footprint, we find that in all subregions both tmin and tmax have increased in response to anthropogenic forcing, the rate being generally greater for tmax. Significant precipitation trends are not detected, whereas PET exhibits significant upward trends in all regions. While PET-normalized precipitation, as well as PDSI, do not exhibit significant trends individually (by variable and region), the fact that most trend downward nevertheless suggests a systematic drying. We conclude that temperature constitutes the principal detectable control on hydroclimatic changes in rangelands within the study area. Although ecosystem responses may be complex, future temperature increases are expected generally to reduce soil water availability. The unforced component of variability is investigated with respect to several key climate indices on both interannual and decadal time scales
Recommended from our members
A framework for the simulation of regional decadal variability for agricultural and other applications
Climate prediction on decadal time scales is currently an active area of research. Although there are indications that predictions from dynamical models may have skill in some regions, assessment of this skill is still underway, and reliable model-based predictions of regional "near-term" climate change, particularly for terrestrial regions, have not yet been demonstrated. Given the absence of such forecasts, synthetic data sequences that capture the statistical properties of observed near-term climate variability have potential value. Incorporation of a climate change component in such sequences can aid in estimating likelihoods for a range of climatic stresses, perhaps lying outside the range of past experience. Such simulations can be used to drive agricultural, hydrological or other application models, enabling resilience testing of adaptation or decision systems. The use of statistically-based methods enables the efficient generation of a large ensemble of synthetic sequences as well as the creation of well-defined probabilistic risk estimates. In this report we discuss procedures for the generation of synthetic climate sequences that incorporate both the statistics of observed variability and expectations regarding future regional climate change. Model fitting and simulation are conditioned by requirements particular to the decadal climate problem. A method for downscaling annualized simulations to the daily time step while preserving both spatial and temporal subannual statistical properties is presented and other possible methods discussed. A "case-study" realization of the proposed framework is described
A climate generator for agricultural planning in southeastern South America
A method is described for the generation of climate scenarios in a form suitable for driving agricultural models. The scenarios are tailored to the region in southeastern South America bounded by 25–40° S, 45–65° W, denoted here as SESA. SESA has been characterized by increasing summer precipitation, particularly during the late 20th century, which, in the context of favorable market conditions, has enabled increases in agricultural production. Since about year 2000, however, the upward tendency appears to have slowed or possibly stopped, raising questions about future climate inputs to regional agricultural yields.
The method is not predictive in the deterministic sense, but rather attempts to characterize uncertainty in near-term future climate, taking into account both forced trends and unforced, natural climate fluctuations. It differs from typical downscaling methods in that GCM information is utilized only at the regional scale, subregional variability being modeled based on the observational record. Output, generated on the monthly time scale, is disaggregated to daily values with a weather generator and used to drive soybean yields in the crop model DSSAT-CSM, for which preliminary results are discussed. The simulations produced permit assessment of the interplay between long-range trends and near-term climate variability in terms of agricultural production
Recommended from our members
Probabilistic Multimodel Regional Temperature Change Projections
Regional temperature change projections for the twenty-first century are generated using a multimodel ensemble of atmosphere–ocean general circulation models. The models are assigned coefficients jointly, using a Bayesian linear model fitted to regional observations and simulations of the climate of the twentieth century. Probability models with varying degrees of complexity are explored, and a selection is made based on Bayesian deviance statistics, coefficient properties, and a classical cross-validation measure utilizing temporally averaged data. The model selected is shown to be superior in predictive skill to a naïve model consisting of the unweighted mean of the underlying atmosphere–ocean GCM (AOGCM) simulations, although the skill differential varies regionally. Temperature projections for the A2 and B1 scenarios from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios are presented
- …