Weather Generators

2020 ◽  
Author(s):  
Shuiqing Yin ◽  
Deliang Chen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Large Scale ◽  
Extreme Values ◽  
Future Climate Change ◽  
Continuous Simulation ◽  
Weather Generators ◽  
Change Impact ◽  
Multiple Variables ◽  
Climate Time Series

Weather generators (WGs) are stochastic models that can generate synthetic climate time series of unlimited length and having statistical properties similar to those of observed time series for a location or an area. WGs can infill missing data, extend the length of climate time series, and generate meteorological conditions for unobserved locations. Since the 1990s WGs have become an important spatial-temporal statistical downscaling methodology and have been playing an increasingly important role in climate-change impact assessment. Although the majority of the existing WGs have focused on simulation of precipitation for a single site, more and more WGs considering correlations among multiple sites, and multiple variables, including precipitation and nonprecipitation variables such as temperature, solar radiation, wind, humidity, and cloud cover have been developed for daily and sub-daily scales. Various parametric, semi-parametric and nonparametric WGs have shown the ability to represent the mean, variance, and autocorrelation characteristics of climate variables at different scales. Two main methodologies including change factor and conditional WGs on large-scale dynamical and thermal dynamical weather states have been developed for applications under a changing climate. However, rationality and validity of assumptions underlining both methodologies need to be carefully checked before they can be used to project future climate change at local scale. Further, simulation of extreme values by the existing WGs needs to be further improved. WGs assimilating multisource observations from ground observations, reanalysis, satellite remote sensing, and weather radar for the continuous simulation of two-dimensional climate fields based on the mixed physics-based and stochastic approaches deserve further efforts. An inter-comparison project on a large ensemble of WG methods may be helpful for the improvement of WGs. Due to the applied nature of WGs, their future development also requires inputs from decision-makers and other relevant stakeholders.

Underestimation of temperature variability in weather generators and implications for the representation of extreme temperatures in downscaled climate change scenarios

2020 ◽  
Author(s):  
Pierluigi Calanca
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Extreme Values ◽  
Climatic Variability ◽  
Model Performance ◽  
Maximum Temperature ◽  
Climate Change Scenarios ◽  
Modes Of Variability ◽  
Weather Generators ◽  
Total Variability

<p>Stochastic weather generators are still widely used for downscaling climate change scenarios, in particular in the context of agricultural and hydrological impact assessments. Their performance is in many respects satisfactory, except perhaps for the fact that they fail to represent climatic variability in an adequate way. This has implications for the representation of extreme values and their statistics. Concerning precipitation, different approaches for amending this situation have proposed in the past, including using more sophisticated models to better simulate the persistence of wet and dry spells, conditioning rainfall-generating parameters on indices of the large-scale atmospheric circulation, or employing autoregressive models to represent year-to-year variations in annual precipitation amounts. With regard to (minimum and maximum) temperature, efforts to address the question of why weather generators underestimate total variability have been less systematic. Based on results obtained with a well-known weather generator (LARS-WG), this contribution aims to discuss which modes of variability are missing and why, elaborate on the implications of underrepresenting temperature variance for the simulation of temperature extremes in downscaled climate change scenarios, and suggest options to tackle the problem and improve the model performance.</p>

scholarly journals Large scale integrated hydrological modelling of the impact of climate change on the water balance with DANUBIA

2011 ◽  
Vol 7 (1) ◽  
pp. 61-70 ◽  
Author(s):  
M. Prasch ◽  
T. Marke ◽  
U. Strasser ◽  
W. Mauser
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Water Balance ◽  
Large Scale ◽  
Regional Scale ◽  
Future Climate ◽  
Future Climate Change ◽  
Impact Of Climate Change ◽  
Ipcc Sres ◽  
The Impact

Abstract. Future climate change will affect the water availability in large areas. In order to derive appropriate adaptation strategies the impact on the water balance has to be determined on a regional scale in a high spatial and temporal resolution. Within the framework of the BRAHMATWINN project the model system DANUBIA, developed within the project GLOWA Danube (GLOWA Danube, 2010; Mauser and Ludwig, 2002), was applied to calculate the water balance components under past and future climate conditions in the large-scale mountain watersheds of the Upper Danube and the Upper Brahmaputra. To use CLM model output data as meteorological drivers DANUBIA is coupled with the scaling tool SCALMET (Marke, 2008). For the determination of the impact of glacier melt water on the water balance the model SURGES (Weber et al., 2008; Prasch, 2010) is integrated into DANUBIA. In this paper we introduce the hydrological model DANUBIA with the tools SCALMET and SURGES. By means of the distributed hydrological time series for the past from 1971 to 2000 the model performance is presented. In order to determine the impact of climate change on the water balance in both catchments, time series from 2011 to 2080 according to the IPCC SRES emission scenarios A2, A1B, B2 and Commitment are analysed. Together with the socioeconomic outcomes (see Chapter 4) the DANUBIA model results provide the basis for the derivation of Integrated Water Resources Management Strategies to adapt to climate change impacts (see Chapter 9 and 10).

Methodology of Climate Change Impact Assessment on Forests

2019 ◽  
pp. 200-219
Author(s):  
Mostafa Jafari
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Global Warming ◽  
Human Life ◽  
Future Climate Change ◽  
Present Climate ◽  
Climate Data ◽  
Empirical Observation ◽  
Climate Change Impact Assessment ◽  
Change Impact

Climate change is one of the challenging issues in various countries. Climate change and climate variability and global warming and its effects on natural resources, plants, animals, and on human life are among the subjects that received the attention of scientists and politicians in recent years. Climate change challenges need to be considered in various dimensions. To both understand the present climate and to predict future climate change, it is necessary to have both theory and empirical observation. Any study of climate change involves the construction (or reconstruction) of time series of climate data. How these climate data vary across time provides a measure (either quantitative or qualitative) of climate change. Types of climate data include temperature, precipitation (rainfall), wind, humidity, evapotranspiration, pressure, and solar irradiance. This chapter explores a methodology of measuring climate change's impact on forests.

Econometrics for Modelling Climate Change

2021 ◽  
Author(s):  
Jennifer L. Castle ◽  
David F. Hendry
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Econometric Modeling ◽  
Complex Data ◽  
Explanatory Variables ◽  
Stochastic Trends ◽  
Trend Breaks ◽  
Abrupt Shifts ◽  
Climate Time Series ◽  
Do So

Shared features of economic and climate time series imply that tools for empirically modeling nonstationary economic outcomes are also appropriate for studying many aspects of observational climate-change data. Greenhouse gas emissions, such as carbon dioxide, nitrous oxide, and methane, are a major cause of climate change as they cumulate in the atmosphere and reradiate the sun’s energy. As these emissions are currently mainly due to economic activity, economic and climate time series have commonalities, including considerable inertia, stochastic trends, and distributional shifts, and hence the same econometric modeling approaches can be applied to analyze both phenomena. Moreover, both disciplines lack complete knowledge of their respective data-generating processes (DGPs), so model search retaining viable theory but allowing for shifting distributions is important. Reliable modeling of both climate and economic-related time series requires finding an unknown DGP (or close approximation thereto) to represent multivariate evolving processes subject to abrupt shifts. Consequently, to ensure that DGP is nested within a much larger set of candidate determinants, model formulations to search over should comprise all potentially relevant variables, their dynamics, indicators for perturbing outliers, shifts, trend breaks, and nonlinear functions, while retaining well-established theoretical insights. Econometric modeling of climate-change data requires a sufficiently general model selection approach to handle all these aspects. Machine learning with multipath block searches commencing from very general specifications, usually with more candidate explanatory variables than observations, to discover well-specified and undominated models of the nonstationary processes under analysis, offers a rigorous route to analyzing such complex data. To do so requires applying appropriate indicator saturation estimators (ISEs), a class that includes impulse indicators for outliers, step indicators for location shifts, multiplicative indicators for parameter changes, and trend indicators for trend breaks. All ISEs entail more candidate variables than observations, often by a large margin when implementing combinations, yet can detect the impacts of shifts and policy interventions to avoid nonconstant parameters in models, as well as improve forecasts. To characterize nonstationary observational data, one must handle all substantively relevant features jointly: A failure to do so leads to nonconstant and mis-specified models and hence incorrect theory evaluation and policy analyses.

scholarly journals Current and future occurrences of health-relevant, compound heat and ozone pollution events in six European ozone-temperature regions 

2021 ◽  
Author(s):  
Sally Jahn ◽  
Elke Hertig
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Large Scale ◽  
Coupled Model ◽  
Ground Level ◽  
Future Climate Change ◽  
Ozone Pollution ◽  
Ground Level Ozone ◽  
Ozone Concentrations ◽  
Elevated Ozone

<p>Air pollution and heat events present two major health risks, both already independently posing a significant threat to human health and life. High levels of ground-level ozone (O<sub>3</sub>) and air temperature often coincide due to the underlying physical relationships between both variables. The most severe health outcome is in general associated with the co-occurrence of both hazards (e.g. Hertig et al. 2020), since concurrent elevated levels of temperature and ozone concentrations represent a twofold exposure and can lead to a risk beyond the sum of the individual effects. Consequently, in the current contribution, a compound approach considering both hazards simultaneously as so-called ozone-temperature (o-t-)events is chosen by jointly analyzing elevated ground-level ozone concentrations and air temperature levels in Europe.</p><p>Previous studies already point to the fact that the relationship of underlying synoptic and meteorological drivers with one or both of these health stressors as well as the correlation between both variables vary with the location of sites and seasons (e.g. Otero et al. 2016; Jahn, Hertig 2020). Therefore, a hierarchical clustering analysis is applied to objectively divide the study domain in regions of homogeneous, similar ground-level ozone and temperature characteristics (o-t-regions). Statistical models to assess the synoptic and large-scale meteorological mechanisms which represent main drivers of concurrent o-t-events are developed for each identified o-t-region.</p><p>Compound elevated ozone concentration and air temperature events are expected to become more frequent due to climate change in many parts of Europe (e.g. Jahn, Hertig 2020; Hertig 2020). Statistical projections of potential frequency shifts of compound o-t-events until the end of the twenty-first century are assessed using the output of Earth System Models (ESMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6).</p><p><em>Hertig, E. (2020) Health-relevant ground-level ozone and temperature events under future climate change using the example of Bavaria, Southern Germany. Air Qual. Atmos. Health. doi: 10.1007/s11869-020-00811-z</em></p><p><em>Hertig, E., Russo, A., Trigo, R. (2020) Heat and ozone pollution waves in Central and South Europe- characteristics, weather types, and association with mortality. Atmosphere. doi: 10.3390/atmos11121271</em></p><p><em>Jahn, S., Hertig, E. (2020) Modeling and projecting health‐relevant combined ozone and temperature events in present and future Central European climate. Air Qual. Atmos. Health. doi: 10.1007/s11869‐020‐009610</em></p><p><em>Otero N., Sillmann J., Schnell J.L., Rust H.W., Butler T. (2016) Synoptic and meteorological drivers of extreme ozone concentrations over Europe. Environ Res Lett. doi: 10.1088/ 1748-9326/11/2/024005</em></p>

scholarly journals What can we learn from long-term groundwater data to improve climate change impact studies?

2011 ◽  
Vol 8 (4) ◽  
pp. 7621-7655 ◽  
Author(s):  
S. Stoll ◽  
H. J. Hendricks Franssen ◽  
R. Barthel ◽  
W. Kinzelbach
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Climate Change Impact ◽  
Large Scale ◽  
Climate Models ◽  
Groundwater Resources ◽  
Groundwater Dynamics ◽  
Impact Studies ◽  
Change Impact

Abstract. Future risks for groundwater resources, due to global change are usually analyzed by driving hydrological models with the outputs of climate models. However, this model chain is subject to considerable uncertainties. Given the high uncertainties it is essential to identify the processes governing the groundwater dynamics, as these processes are likely to affect groundwater resources in the future, too. Information about the dominant mechanisms can be achieved by the analysis of long-term data, which are assumed to provide insight in the reaction of groundwater resources to changing conditions (weather, land use, water demand). Referring to this, a dataset of 30 long-term time series of precipitation dominated groundwater systems in northern Switzerland and southern Germany is collected. In order to receive additional information the analysis of the data is carried out together with hydrological model simulations. High spatio-temporal correlations, even over large distances could be detected and are assumed to be related to large-scale atmospheric circulation patterns. As a result it is suggested to prefer innovative weather-type-based downscaling methods to other stochastic downscaling approaches. In addition, with the help of a qualitative procedure to distinguish between meteorological and anthropogenic causes it was possible to identify processes which dominated the groundwater dynamics in the past. It could be shown that besides the meteorological conditions, land use changes, pumping activity and feedback mechanisms governed the groundwater dynamics. Based on these findings, recommendations to improve climate change impact studies are suggested.

scholarly journals Impact of climate change on groundwater point discharge: backflooding of karstic springs (Loiret, France)

2011 ◽  
Vol 8 (2) ◽  
pp. 2235-2262
Author(s):  
E. Joigneaux ◽  
P. Albéric ◽  
H. Pauwels ◽  
C. Pagé ◽  
L. Terray ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Groundwater Quality ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Weather Type ◽  
Future Climate Change ◽  
Impact Of Climate Change ◽  
The Impact ◽  
Large Scale Atmospheric Circulation

Abstract. Under certain hydrological conditions it is possible for spring flow in karst systems to be reversed. When this occurs, the resulting invasion by surface water, i.e. the backflooding, represents a serious threat to groundwater quality because the surface water could well be contaminated. Here we examine the possible impact of future climate change on the occurrences of backflooding in a specific karst system, having first established the occurrence of such events in the selected study area over the past 40 yr. It would appear that backflooding has been more frequent since the 1980s, and that it is apparently linked to river flow variability on the pluri-annual scale. The avenue that we adopt here for studying recent and future variations of these events is based on a downscaling algorithm relating large-scale atmospheric circulation to local precipitation spatial patterns. The large-scale atmospheric circulation is viewed as a set of quasi-stationary and recurrent states, called weather types, and its variability as the transition between them. Based on a set of climate model projections, simulated changes in weather-type occurrence for the end of the century suggests that backflooding events can be expected to decrease in 2075–2099. If such is the case, then the potential risk for groundwater quality in the area will be greatly reduced compared to the current situation. Finally, our results also show the potential interest of the weather-type based downscaling approach for examining the impact of climate change on hydrological systems.

scholarly journals Climate change impact on streamflow in large-scale river basins: projections and their uncertainties sourced from GCMs and RCP scenarios

2018 ◽  
Vol 379 ◽  
pp. 139-144 ◽  
Author(s):  
Olga N. Nasonova ◽  
Yeugeniy M. Gusev ◽  
Evgeny E. Kovalev ◽  
Georgy V. Ayzel
Keyword(s):  
Climate Change ◽  
River Runoff ◽  
Climate Change Impact ◽  
Large Scale ◽  
River Basins ◽  
Surface Model ◽  
Second Phase ◽  
Rcp Scenarios ◽  
Data Set ◽  
Change Impact

Abstract. Climate change impact on river runoff was investigated within the framework of the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP2) using a physically-based land surface model Soil Water – Atmosphere – Plants (SWAP) (developed in the Institute of Water Problems of the Russian Academy of Sciences) and meteorological projections (for 2006–2099) simulated by five General Circulation Models (GCMs) (including GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M) for each of four Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Eleven large-scale river basins were used in this study. First of all, SWAP was calibrated and validated against monthly values of measured river runoff with making use of forcing data from the WATCH data set and all GCMs' projections were bias-corrected to the WATCH. Then, for each basin, 20 projections of possible changes in river runoff during the 21st century were simulated by SWAP. Analysis of the obtained hydrological projections allowed us to estimate their uncertainties resulted from application of different GCMs and RCP scenarios. On the average, the contribution of different GCMs to the uncertainty of the projected river runoff is nearly twice larger than the contribution of RCP scenarios. At the same time the contribution of GCMs slightly decreases with time.

scholarly journals Atmospheric Dynamics Feedback: Concept, Simulations, and Climate Implications

2018 ◽  
Vol 31 (8) ◽  
pp. 3249-3264 ◽  
Author(s):  
Michael P. Byrne ◽  
Tapio Schneider
Keyword(s):  
Climate Change ◽  
Atmospheric Circulation ◽  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Atmospheric Dynamics ◽  
Future Climate Change ◽  
Coupled Models ◽  
Near Surface ◽  
Circulation Changes

AbstractThe regional climate response to radiative forcing is largely controlled by changes in the atmospheric circulation. It has been suggested that global climate sensitivity also depends on the circulation response, an effect called the “atmospheric dynamics feedback.” Using a technique to isolate the influence of changes in atmospheric circulation on top-of-the-atmosphere radiation, the authors calculate the atmospheric dynamics feedback in coupled climate models. Large-scale circulation changes contribute substantially to all-sky and cloud feedbacks in the tropics but are relatively less important at higher latitudes. Globally averaged, the atmospheric dynamics feedback is positive and amplifies the near-surface temperature response to climate change by an average of 8% in simulations with coupled models. A constraint related to the atmospheric mass budget results in the dynamics feedback being small on large scales relative to feedbacks associated with thermodynamic processes. Idealized-forcing simulations suggest that circulation changes at high latitudes are potentially more effective at influencing global temperature than circulation changes at low latitudes, and the implications for past and future climate change are discussed.

Sign in / Sign up

Export Citation Format

Share Document