Atmospheric Moisture Residence Times and Cycling: Implications
... precipitates out comes from horizontal transport versus local evaporation, referred to as ‘recycling’. The results depend greatly on the scale of the domain under consideration and global maps of the recycling for annual means are produced for 500 km scales for which global recycling is 9.6%, consis ...
... precipitates out comes from horizontal transport versus local evaporation, referred to as ‘recycling’. The results depend greatly on the scale of the domain under consideration and global maps of the recycling for annual means are produced for 500 km scales for which global recycling is 9.6%, consis ...
Mediterranean Sea response to climate change in an
... development of the sea-ice component. It is important to notice that there are not enough elements to assess if one of those simulations is more realistic. Here we consider these two simulations as plausible. ...
... development of the sea-ice component. It is important to notice that there are not enough elements to assess if one of those simulations is more realistic. Here we consider these two simulations as plausible. ...
Single-Period Models (Discrete Demand)
... There are two cases: 1. Backorder - if the excess demand is backlogged and fulfilled in a future period, a backorder cost is charged. Backorder cost is estimated from ...
... There are two cases: 1. Backorder - if the excess demand is backlogged and fulfilled in a future period, a backorder cost is charged. Backorder cost is estimated from ...
The Relationship between Land–Ocean Surface Temperature
... more rapidly than SSTs. We know that this does not happen, however, as LST and SST anomalies stay quite close to a constant ratio: there is only a small rapid adjustment in LST. If we assume for now that land and ocean temperatures are merely a function of heat stored (this may not be the case for a ...
... more rapidly than SSTs. We know that this does not happen, however, as LST and SST anomalies stay quite close to a constant ratio: there is only a small rapid adjustment in LST. If we assume for now that land and ocean temperatures are merely a function of heat stored (this may not be the case for a ...
Efficient construction of reversible jump Markov chain Monte Carlo
... a generic way. Although there has been considerable progress in this area for fixed dimension sampling problems (see for example Gelman et al. (1996) and Roberts and Rosenthal (1998)), most of the available statistical applications of reversible jump techniques rely on various strategies of empirical ...
... a generic way. Although there has been considerable progress in this area for fixed dimension sampling problems (see for example Gelman et al. (1996) and Roberts and Rosenthal (1998)), most of the available statistical applications of reversible jump techniques rely on various strategies of empirical ...
Observed and simulated full-depth ocean heat
... than 90 % of the excess heat is stored in the ocean and is manifested by ocean warming (Loeb et al., 2012; Balmaseda et al., 2013; Rhein et al., 2013; Trenberth et al., 2014), i.e., an increase in global ocean heat content (OHC; Lyman et al., 2010; Levitus et al., 2012; Abraham et al., 2013). Due to ...
... than 90 % of the excess heat is stored in the ocean and is manifested by ocean warming (Loeb et al., 2012; Balmaseda et al., 2013; Rhein et al., 2013; Trenberth et al., 2014), i.e., an increase in global ocean heat content (OHC; Lyman et al., 2010; Levitus et al., 2012; Abraham et al., 2013). Due to ...
The Atmospheric Energy Constraint on Global
... entering the atmosphere through its top (the TOA) and its bottom (the earth’s surface). We can quantify these terms by examining the multimodel mean from 10 years of CMIP5 simulations. In these simulations, on average, LP is 85 W m22, SH is 20 W m22, and R is 2105 W m22, and these terms balance sinc ...
... entering the atmosphere through its top (the TOA) and its bottom (the earth’s surface). We can quantify these terms by examining the multimodel mean from 10 years of CMIP5 simulations. In these simulations, on average, LP is 85 W m22, SH is 20 W m22, and R is 2105 W m22, and these terms balance sinc ...
for European Journal of Plant Pathology Manuscript Draft
... eventuality and component, but I believe the authors have addressed most issues as comprehensively and realistically as they can. I think there are a few issues of editing/clarification. Page 2, lines 45-50. I know what you mean, but the sentence is slightly confusing. Crop yields may also be affec ...
... eventuality and component, but I believe the authors have addressed most issues as comprehensively and realistically as they can. I think there are a few issues of editing/clarification. Page 2, lines 45-50. I know what you mean, but the sentence is slightly confusing. Crop yields may also be affec ...
Climate Sensitivity - Home page 350.me.uk
... Thus no ocean transport feedback is permitted in these experiments. Our rationale for this approach as a f i r s t step is i t s simplicity for analysis, and the fact that i t permits a realistic atmospheric simulation. Ocean ice cover is also computed in the experiments described here on the basis ...
... Thus no ocean transport feedback is permitted in these experiments. Our rationale for this approach as a f i r s t step is i t s simplicity for analysis, and the fact that i t permits a realistic atmospheric simulation. Ocean ice cover is also computed in the experiments described here on the basis ...
Sensitivity of terrestrial precipitation trends to the structural evolution
... SSTs, we perform a “Principal Uncertainty Pattern” (PUP) [Langenbrunner et al., 2013] analysis utilizing both empirical orthogonal function (EOF) analysis and Canonical Correlation Analysis (CCA). Generically, EOF analysis is a univariate variance decomposition algorithm designed to maximize the var ...
... SSTs, we perform a “Principal Uncertainty Pattern” (PUP) [Langenbrunner et al., 2013] analysis utilizing both empirical orthogonal function (EOF) analysis and Canonical Correlation Analysis (CCA). Generically, EOF analysis is a univariate variance decomposition algorithm designed to maximize the var ...
Semiarid watershed response in central New Mexico and its
... model: Agustin, Brushy Mountain, Datil, Laguna, and Socorro (Fig. 3). Each gauge provides hourly measurements with record lengths varying from 4 to 30 years (Table 2). The records were used to identify consecutive rainfall periods, estimate their average intensity over the event duration and determi ...
... model: Agustin, Brushy Mountain, Datil, Laguna, and Socorro (Fig. 3). Each gauge provides hourly measurements with record lengths varying from 4 to 30 years (Table 2). The records were used to identify consecutive rainfall periods, estimate their average intensity over the event duration and determi ...
Numerical weather prediction
Numerical weather prediction uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Though first attempted in the 1920s, it was not until the advent of computer simulation in the 1950s that numerical weather predictions produced realistic results. A number of global and regional forecast models are run in different countries worldwide, using current weather observations relayed from radiosondes, weather satellites and other observing systems as inputs.Mathematical models based on the same physical principles can be used to generate either short-term weather forecasts or longer-term climate predictions; the latter are widely applied for understanding and projecting climate change. The improvements made to regional models have allowed for significant improvements in tropical cyclone track and air quality forecasts; however, atmospheric models perform poorly at handling processes that occur in a relatively constricted area, such as wildfires.Manipulating the vast datasets and performing the complex calculations necessary to modern numerical weather prediction requires some of the most powerful supercomputers in the world. Even with the increasing power of supercomputers, the forecast skill of numerical weather models extends to about only six days. Factors affecting the accuracy of numerical predictions include the density and quality of observations used as input to the forecasts, along with deficiencies in the numerical models themselves. Post-processing techniques such as model output statistics (MOS) have been developed to improve the handling of errors in numerical predictions.A more fundamental problem lies in the chaotic nature of the partial differential equations that govern the atmosphere. It is impossible to solve these equations exactly, and small errors grow with time (doubling about every five days). Present understanding is that this chaotic behavior limits accurate forecasts to about 14 days even with perfectly accurate input data and a flawless model. In addition, the partial differential equations used in the model need to be supplemented with parameterizations for solar radiation, moist processes (clouds and precipitation), heat exchange, soil, vegetation, surface water, and the effects of terrain. In an effort to quantify the large amount of inherent uncertainty remaining in numerical predictions, ensemble forecasts have been used since the 1990s to help gauge the confidence in the forecast, and to obtain useful results farther into the future than otherwise possible. This approach analyzes multiple forecasts created with an individual forecast model or multiple models.