Prediction Of Climate Variability Research Articles

APPLICATION OF SIMPLE EXPONENTIAL SMOOTHING METHOD FOR TEMPERATURE FORECASTING IN TWO MAJOR CITIES OF THE PUNJAB, PAKISTAN

Weather forecasting has been getting more attention from researchers and becoming influential factor for effective policy and planning at local and global level. Climatic variations have been directly affected by global warming and happened to rise atmospheric temperature; as a resultant about 0.74°C temperature of earth has been increased during last 100 years. In Pakistan, daily environmental temperature has been increasing; on average there is 0.6°C rise of daily environmental temperature has been observed. This research has been focused on the modeling and forecasting about daily temperature of Faisalabad and Lahore districts of Punjab, Pakistan. The results showed that simple exponential technique is more appropriate technique for modeling the temperature data as compared to other methods such as Holt’s exponential and Holt’s winter exponential techniques. The forecasting of temperature data was made on the optimum value of smoothing constant. The selection of optimal smoothing constant was based on the minimum values of mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). The application of simple exponential technique is therefore recommended for prediction of climatic variables like rainfall, humidity and wind speed because of its forecasting accuracy for short term period. This research will be helpful to researchers and policy makers who are associated with environment-based work.

Rainfall and Atmospheric Temperature against the Other Climatic Factors: A Case Study from Colombo, Sri Lanka

Climate prediction is given a high priority by many countries due to its importance in mitigation of extreme weather conditions. However, the prediction is not an easy task as the climatic parameters not only show spatial variations but also temporal variations. In addition, the climatic parameters are interrelated. To overcome these difficulties, soft computing techniques are widely used in prediction of climate variables with respect to the other variables. On the other hand, Colombo, Sri Lanka, is experiencing adverse or extreme weather conditions over the last few years. However, a climate prediction study is yet to be carried out in this tropical climatic zone. Therefore, this paper presents a study, identifying relationships between the two most impacted climate parameters (atmospheric temperature and rainfall) and other climatic parameters. Artificial neural network (ANN) models are developed to define the relationships and then to predict the atmospheric temperature as a function of other parameters including monthly rainfall, minimum and maximum relative humidity, and average wind speed. Same analysis is carried out to define the prediction model to the monthly rainfall. The best algorithm out of several other ANN algorithms is chosen for the analyses. Results revealed that the atmospheric temperature in Colombo can be presented with respect to the other climatic variables. However, the rainfall does not show a greater relationship with the other climatic parameters.

Climate Variability over the Maritime Continent and Its Role in Global Climate Variation: A Review

The Maritime Continent (MC) consists of multiple islands with varying sizes and topography, and surrounding seas. It is characterized by rainfall (convection) variability on multiple spatial and temporal scales. Various large-scale atmospheric, oceanic, and coupled climate systems, such as the El Nino–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Madden–Julian Oscillation (MJO), and cold surge, exert significant influences on the spatio-temporal complexity of the MC climate and climate variability. As a major tropical heat source located within the warmest oceanic area (the western Pacific warm pool), the MC has been identified as a region of great importance for climate variation on the global scale. However, prediction of climate variability over the MC and its surrounding areas and the relationships to large-scale atmospheric circulation patterns are big challenges, even for state-of-the-art climate models. In this paper, we provide a thorough review on current understanding of the spatiotemporal complexity and prediction of climate variability over this important region, and its influence on global climate variation.

Prediction of climate variables by comparing the knearest neighbor method and MIROC5 outputs in an arid environment

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 77:99-114 (2019) - DOI: https://doi.org/10.3354/cr01545 Prediction of climate variables by comparing the k-nearest neighbor method and MIROC5 outputs in an arid environment Hamid Reza Golkar Hamzee Yazd1, Nasrin Salehnia2,*, Sohrab Kolsoumi2, Gerrit Hoogenboom3 1Ferdows Branch, Islamic Azad University, PO Box 9771-848664, Ferdows, Iran 2Ferdowsi University of Mashhad, PO Box 9177-949207, Mashhad, Iran 3Institute for Sustainable Food Systems, University of Florida, PO Box 110570, Gainesville, Florida, USA *Corresponding author: salehnia61@gmail.com ABSTRACT: The goal of this study was to compare the ability of the k-nearest neighbors (k-NN) approach and the downscaled output from the MIROC5 model for generating daily precipitation (mm) and daily maximum and minimum temperature (Tmax and Tmin; °C) for an arid environment. For this study, data from the easternmost province of Iran, South Khorasan, were used for the period 1986 to 2015. We also used an ensemble method to decrease the uncertainty of the k-NN approach. Although, based on an initial evaluation, MIROC5 had better results, we also used the output results of k-NN alongside the MIROC5 data to generate future weather data for the period 2018 to 2047. Nash-Sutcliffe efficiency (NSE) between MIROC5 estimates and observed monthly Tmax ranged from 0.86 to 0.92, and from 0.89 to 0.93 for Tmin over the evaluation period (2006-2015). k-NN performed less well, with NSE between k-NN estimates and observed Tmax ranging from 0.54 to 0.64, and from 0.75 to 0.78 for Tmin. The MIROC5 simulated precipitation was close to observed historical values (-0.06 < NSE < 0.07), but the k-NN simulated precipitation was less accurate (-0.36 < NSE < -0.14). For the studied arid regions, the k-NN precipitation results compared poorly to the MIROC5 downscaling results. MIROC5 predicts increases in monthly Tmin and Tmax in summer and autumn and decreases in winter and spring, and decreases in winter monthly precipitation under RCP4.5 over the 2018-2047 period of this study. This study showed that the k-NN method should be expected to have inaccurate results for generating future data in comparison to the outputs of the MIROC5 model for arid environments. KEY WORDS: RCP4.5 · Statistical downscaling · Delta method · Ensemble · LARS-WG · Lut Desert Full text in pdf format Supplementary material PreviousNextCite this article as: Golkar Hamzee Yazd HR, Salehnia N, Kolsoumi S, Hoogenboom G (2019) Prediction of climate variables by comparing the k-nearest neighbor method and MIROC5 outputs in an arid environment. Clim Res 77:99-114. https://doi.org/10.3354/cr01545 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 77, No. 2. Online publication date: February 21, 2019 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2019 Inter-Research.

SPATIAL PREDICTION OF AIR TEMPERATURE IN EAST CENTRAL ANATOLIA OF TURKEY

Abstract. Air temperature is an essential component of the factors used in landscape planning. At similar topographic conditions, vegetation may show considerable differences depending on air temperature and precipitation. In large areas, measuring temperature is a cost and time-consuming work. Therefore, prediction of climate variables at unmeasured sites at an acceptable accuracy is very important in regional resource planning. In addition, use a more proper prediction method is crucial since many different prediction techniques yield different performance in different landscape and geographical conditions. We compared inverse distance weighted (IDW), ordinary kriging (OK), and ordinary cokriging (OCK) to predict air temperature at unmeasured sites in Malatya region (East Central Anatolia) of Turkey. Malatya region is the most important apricot production area of Turkey and air temperature is the most important factor determining the apricot growing zones in this region. We used mean monthly temperatures from 1975 to 2010 measured at 28 sites in the study area and predicted temperature with IDW, OC, and OCK techniques, mapped temperature in the region, and tested the reliability of these maps. The OCK with elevation as an auxiliary variable occurred the best procedure to predict temperature against the criteria of model efficiency and relative root mean squared error.

On the prediction of persistent processes using the output of deterministic models

ABSTRACTA problem frequently met in engineering hydrology is the forecasting of hydrological variables conditional on their historical observations and the hindcasts and forecasts of a deterministic model. On the contrary, it is a common practice for climatologists to use the output of general circulation models (GCMs) for the prediction of climatic variables despite their inability to quantify the uncertainty of the predictions. Here we apply the well-established Bayesian processor of forecasts (BPF) for forecasting hydroclimatic variables using stochastic models through coupling them with GCMs. We extend the BPF to cases where long-term persistence appears, using the Hurst-Kolmogorov process (HKp, also known as fractional Gaussian noise) and we investigate its properties analytically. We apply the framework to calculate the distributions of the mean annual temperature and precipitation stochastic processes for the time period 2016–2100 in the United States of America conditional on historical observations and the respective output of GCMs.

On the inherent predictability of precipitation across the United States

Predictability of climate variables is known to be limited to few days up to few weeks due to the inherent chaotic nature and resulting sensitivity to initial conditions. However, such generalization of limited predictability is cautioned because of the highly nonlinear nature and the known influence of localized causal factors on many climate variables. Additionally, even though an improvement in predictability is expected with coarsening in spatial and temporal resolutions, the extent and rate of this expected improvement is still unexplored. This study investigates the spatial distribution of predictability of daily precipitation across the USA. The emphasis is on determining the rate of increase in predictability with spatio-temporal averaging, by defining three predictability statistics (maximum predictability, predictive error, and predictive instability) based on the nonlinear finite-time Lyapunov exponent. From our analyses, we find that predictability increases monotonically with temporal averaging, while spatial averaging has minimal influence, pointing to the possible spatially invariant nature of precipitation dynamics. Modeling the precipitation dynamics at relatively coarser scales of 1° × 1° and higher temporal scales of 5–10 days could markedly improve the predictability statistics. Significant changes in the predictability characteristics of daily precipitation across large areas of the USA and associated non-stationarity are also identified over the 1948–2006 period. This is consistent with sudden changes in the overall nature of precipitation over time, which include a reduction in non-rainy days, an increase in signal-to-noise ratio, an increase in average precipitation events, and an increase in extremes.

High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6

Abstract. Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest both the possibility of significant changes in large-scale aspects of circulation as well as improvements in small-scale processes and extremes. However, such high-resolution global simulations at climate timescales, with resolutions of at least 50 km in the atmosphere and 0.25° in the ocean, have been performed at relatively few research centres and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other model intercomparison projects (MIPs). Increases in high-performance computing (HPC) resources, as well as the revised experimental design for CMIP6, now enable a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability. The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal-resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950–2050, with the possibility of extending to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulations. HighResMIP thereby focuses on one of the CMIP6 broad questions, “what are the origins and consequences of systematic model biases?”, but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.

Simulation and Prediction of Climate Variability and Assessment of the Response of Water Resources in a Typical Watershed in China

The assessment of water resource responses to climate change is required in water resource planning and management, protecting environmental quality, and managing watersheds. This study modeled surface runoff and baseflow responses to variations in precipitation (0%, ±10%, and ±20%) and temperature (0 °C, ±1 °C, and ±2 °C) in 25 types of scenarios in the Lanhe Watershed (1140 km2), which possesses the typical hydrological and meteorological characteristics of the Loess Plateau in China. The study is based on the Soil and Water Assessment Tool (SWAT), which was calibrated and validated using the coefficient of determination (R2), Nash-Suttcliffe (Ens), and Percent bias (PBIAS), using the observed streamflow of Shangjingyou Station, a unique gauging station in the study area. The model was calibrated with daily streamflow, from 1967 to 1996, and then validated from 1997 to 2011. R2, Ens, and PBIAS were 0.95 and 0.84, 0.78 and 0.72, and 0.6% and −9.1% in annual and monthly calibration periods, 0.90 and 0.78, 0.74 and 0.67, and 22.1% and 18.8% in annual and monthly validation periods, and the overall performance ratings was “satisfactory”. The assessment indicates that surface runoff is likely to be more affected than baseflow when altering temperatures and precipitation, and the noticeable changes of surface and baseflow are from June to September and October to November, respectively. Results also indicate that surface runoff and baseflow are very sensitive to the projected reduction in temperature, rather than to an increase of temperature, while precipitation is a constant. In turn, when the temperature is a constant, the surface runoff is sensitive to the projected increase in precipitation and the baseflow is sensitive to the decrease in precipitation.

Comparative Evaluation of Performances of Two Versions of NCEP Climate Forecast System in Predicting Winter Precipitation over India

The precipitation during winter (December through February) over India is highly variable in terms of time and space. Maximum precipitation occurs over the Himalaya region, which is important for water resources and agriculture sectors over the region and also for the economy of the country. Therefore, in the present global warming era, the realistic prediction of winter precipitation over India is important for planning and implementing agriculture and water management strategies. The National Centers for Environmental Prediction (NCEP) issued the operational prediction of climatic variables in monthly to seasonal scale since 2004 using their first version of fully coupled global climate model known as Climate Forecast System (CFSv1). In 2011, a new version of CFS (CFSv2) was introduced with the incorporation of significant changes in older version of CFS (CFSv1). The new version of CFS is required to compare in detail with the older version in the context of simulating the winter precipitation over India. Therefore, the current study presents a detailed analysis on the performance of CFSv2 as compared to CFSv1 for the winter precipitation over India. The hindcast runs of both CFS versions from 1982 to 2008 with November initial conditions are used and the model’s precipitation is evaluated with that of India Meteorological Department (IMD). The models simulated wind and geopotential height against the National Center for Atmospheric Research (NCEP–NCAR) reanalysis-2 (NNRP2) and remote response patterns of SST against Extended Reconstructed Sea Surface Temperatures version 3b (ERSSTv3b) are examined for the same period. The analyses of winter precipitation revealed that both the models are able to replicate the patterns of observed climatology; interannual variability and coefficient of variation. However, the magnitude is lesser than IMD observation that can be attributed to the model’s inability to simulate the observed remote response of sea surface temperatures to all India winter precipitation. Of the two, CFSv1 is appreciable in capturing year-to-year variations in observed winter precipitation while CFSv2 failed in simulating the same. CFSv1 has accounted for less mean bias and RMSE errors along with good correlations and index of agreements than CFSv2 for predicting winter precipitation over India. In addition, the CFSv1 is also having a high probability of detection in predicting different categories (normal, excess and deficit) of observed winter precipitation over India.

Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models

Abstract The present paper is the second part of a two-part study on empirical modeling and prediction of climate variability. This paper deals with spatially distributed data, as opposed to the univariate data of Part I. The choice of a basis for effective data compression becomes of the essence. In many applications, it is the set of spatial empirical orthogonal functions that provides the uncorrelated time series of principal components (PCs) used in the learning set. In this paper, the basis of the learning set is obtained instead by applying multichannel singular-spectrum analysis to climatic time series and using the leading spatiotemporal PCs to construct a reduced stochastic model. The effectiveness of this approach is illustrated by predicting the behavior of the Jin–Neelin–Ghil (JNG) hybrid seasonally forced coupled ocean–atmosphere model of El Niño–Southern Oscillation. The JNG model produces spatially distributed and weakly nonstationary time series to which the model reduction and prediction methodology is applied. Critical transitions in the hybrid periodically forced coupled model are successfully predicted on time scales that are substantially longer than the duration of the learning sample.

Optimizing diet and pasture management to improve sustainability of U.S. beef production

System sustainability balances environmental impact, economic viability and social acceptability. Assessment methods to investigate impacts of enterprise management and consumer decisions on sustainability of beef cattle operations are critically needed. Tools of this nature are especially important given the predictions of climate variability and the dependence of beef production systems on forage availability. A model optimizing nutritional and pasture management was created to examine the environmental impact of beef production. The model integrated modules calculating cradle-to-farm gate environmental impact, diet cost, pasture growth and willingness to pay (WTP). Least-cost diet and pasture management options served as a baseline to which environmental-impact reducing scenarios were compared. Economic viability was ensured by a constraint limiting change in diet cost to less than consumer WTP. Increased WTP was associated with improved social acceptability. Model outputs were evaluated by comparing to published data. Sensitivity analysis of the WTP constraint was conducted. A series of scenarios then examined how forecasted changes in precipitation patterns might alter forage supply and opportunities to reduce environmental impact in three regions in the United States. On a national scale, single-objective optimization indicated individual reductions in greenhouse gases (GHG), land use and water use of 3.6%, 5.4% and 4.3% were possible by changing diets. Multi-objective optimization demonstrated that GHG, land and water use could be simultaneously reduced by 2.3%. To achieve this change, cow–calf diets relied on grass hay, continuously- or rotationally-grazed irrigated and fertilized pasture as well as rotationally-grazed pasture. Stocker diets used rotationally-grazed, irrigated and fertilized pasture and feedlot diets used grass hay as a forage source. The model was sensitive to consumer WTP. When alternative precipitation patterns were simulated, opportunities to decrease the environmental impact of beef production in the Pacific Northwest and Texas were reduced by precipitation changes; whereas opportunities in the Midwest improved. Economic viability, rather than biological limitations, reduced the potential to improve environmental impact under future precipitation scenarios. Decreased spring rainfall resulted in lower pasture yields and required greater use of stored forages. Related increases in diet cost reduced opportunities to appropriate funds toward investment in environmental-impact reducing pasture management strategies. The model developed in this study is a robust tool that can be used to assess the impacts of enterprise management and consumer decisions on beef production sustainability.

On the use of niching genetic algorithms for variable selection in solar radiation estimation

Abstract Prediction of climatic variables, in particular those related to wind and solar radiation, has developed a huge interest in recent years, mainly due to its applications to renewable energy. In many cases there is a large number of factors that influence the climatic variable of interest, and the researcher chooses the most relevant ones (based on previous knowledge of the region, availability, etc.) and runs a series of experiments combining the available data in order to find the combination that provides the best prediction. In this work we present two applications of Niching Genetic Algorithms to solve the problem of selection of variables for the estimation of Solar Radiation. On one hand, this methodology is able to estimate a given climatic variable using databases with missing data, since the algorithm can compensate it by the use of others. On the other hand, we present a methodology that allows us to select the relevant input variables for a given climatic variable estimation or prediction problem, in a systematic way, using the same Genetic Algorithm with different parameters. Both methods were tested in the estimation of daily Global Solar Radiation in El Colmenar (Tucuman, Argentina), using linear regression on data from 14 weather stations spread along the north of Argentina. The results obtained show that the methodology is appropriate, providing an RMSE = 2.36 [MJ/m2] and R = 0.926 using an average of 64 out of 329 initial variables, on a 70 individuals/85 generations combination. For a 200 individuals/150 generations combination it obtained an RMSE = 2.34 [MJ/m2] and R = 0.928 using an average of 54 variables.

Climate Literacy as a Foundation for Progress in Predicting and Adapting to the Climate of the Coming Decades

No Abstract available.

Case studies in interannual to decadal climate predictability

The predictability of ocean and climate variables is investigated, using a perfect model-based case study approach that recognises that predictability is dependent on the initial climate state. In line with previous studies, large scale ocean variables show predictability for several years or more; by contrast, the predictability of climate variables is generally limited to 2 years at most. That predictability shows high sensitivity to the initial state is demonstrated by predictable climate signals arising in different regions, variables and seasons for different initial conditions. The predictability of climate variables in the second year is of particular interest, because this is beyond the timescale that is usually considered to be the limit of seasonal predictability. For different initial conditions, second year predictability is found in: temperatures in southeastern North America (winter) and western Europe (winter and summer), and precipitation in India (summer monsoon) and in the tropical South Atlantic. Second year predictability arises either from persistence of large-scale sea surface temperature (SST) and related ocean heat content anomalies, particularly in regions such as the North Atlantic and Southern Ocean, or from mechanisms that involve El Nino Southern Oscillation (ENSO) dynamics.

A methodology for sensitivity analysis in inverse problems—application to a palaeoclimate study

AbstractWe present a methodology for implementing sensitivity analysis in the case of inverse problems. This is motivated by a problem in palaeoclimatology, where interest lies in reconstruction of past climates from fossil data. In such situations fossil data are modelled as functions of climate variables, since in general ecological terms, variations in climate drive variations in fossil. However, prediction of climate variables are of interest, indicating the inverse nature of the problem.Technically, given a data set {xi, yi}; i = 1, … , n, and a probability distribution p(yi | xi, θ), where θ is the set of model parameters, the problem is ‘inverse’ if, given a new observed y, prediction of the corresponding unknown x is required. On the other hand, the more usual forward problem considers prediction of y for given x. Sensitivity analysis in forward problems is about examining if the results are robust with respect to changes of the prior on θ. This has received a lot of attention in the statistical literature. However, in the case of inverse problems, a prior on x is required, in addition to the prior on θ. This complicates sensitivity analysis, at least operationally, and, to our knowledge, there exists no work in the literature that addresses this. In this paper, we propose a new methodology for sensitivity analysis in inverse problems, and discuss its application to a real, complex, palaeoclimate problem. Copyright © 2007 John Wiley &amp; Sons, Ltd.

The Argo Project: Global Ocean Observations for Understanding and Prediction of Climate Variability

Abstract : Phase 1 (9/99 - 9/02) and Phase 2 (7/00 - 6/02) of US Argo provided a total of 187 CTD profiling floats in the Pacific, Atlantic and Indian Oceans. Objectives were to demonstrate technological capabilities for fabrication and for deployment of float arrays in remote ocean locations (Phase 1) and to demonstrate the capability for manufacture and deployment of large float arrays (Phase 2). Recent technology developments in profiling floats were also implemented, including new generation salinity sensors, improved depth capability, and deployment techniques using fast ships and aircraft. Development of the U.S. Argo Data System was part of Phase 1, on a collaborative basis with international partners. Objectives are to make all Argo data publicly available within a day of collection, applying automated quality control procedures consistent with international Argo practices. Data appropriate for research applications and for comparison with climate change models are not available for several months since they need quality control by salinity experts and evaluation of data over many (10 day) float cycles. Phase 3 is a 5-year project (8/01 - 6/06) including full implementation of the US component of Argo. This report includes Phase 3/Year 2, which deployed 290 CTD profiling floats during CY2003 plus support for these deployments, data management activities and for national and international coordination of Argo. Float deployments targeted the Atlantic, Pacific, Indian, and Southern Oceans. Priorities for US float deployment are set by the US Argo Advisory Panel.