A Robust but Spurious Pattern of Climate Change in Model Projections over the Tropical Indian Ocean

2016 ◽  
Vol 29 (15) ◽  
pp. 5589-5608 ◽  
Author(s):  
Gen Li ◽  
Shang-Ping Xie ◽  
Yan Du
Keyword(s):  
Climate Change ◽  
Indian Ocean ◽  
Climate Models ◽  
Equatorial Indian Ocean ◽  
Climate Projections ◽  
Model Errors ◽  
Ipcc Report ◽  
Warming Pattern ◽  
The Future ◽  
The Mean

Abstract Climate models consistently project reduced surface warming over the eastern equatorial Indian Ocean (IO) under increased greenhouse gas (GHG) forcing. This IO dipole (IOD)-like warming pattern, regarded as robust based on consistency among models by the new Intergovernmental Panel on Climate Change (IPCC) report, results in a large increase in the frequency of extreme positive IOD (pIOD) events, elevating the risk of climate and weather disasters in the future over IO rim countries. These projections, however, do not consider large model biases in both the mean state and interannual IOD variance. In particular, a “present–future relationship” is identified between the historical simulations and representative concentration pathway (RCP) 8.5 experiments from phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble: models with an excessive IOD amplitude bias tend to project a strong IOD-like warming pattern in the mean and a large increase in extreme pIOD occurrences under increased GHG forcing. This relationship links the present simulation errors to future climate projections, and is also consistent with our understanding of Bjerknes ocean–atmosphere feedback. This study calibrates regional climate projections by using this present–future relationship and observed IOD amplitude. The results show that the projected IOD-like pattern of mean changes and frequency increase of extreme pIOD events are largely artifacts of model errors and unlikely to emerge in the future. These results illustrate that a robust projection may still be biased and it is important to consider the model bias effect.

scholarly journals Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2266 ◽  
Author(s):  
Enrique Soriano ◽  
Luis Mediero ◽  
Carlos Garijo
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Control Period ◽  
Flood Frequency ◽  
Climate Projections ◽  
The Future ◽  
Flood Quantiles ◽  
The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

Retooling climate science for risk assessment

2021 ◽  
Author(s):  
Ben Sanderson ◽  
Saloua Peatier ◽  
Laurent Terray
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Science ◽  
Climate Projections ◽  
Climate Feedbacks ◽  
Climate Assessment ◽  
Global Issue ◽  
Ipcc Report ◽  
Tail Behaviour ◽  
Future Work

<p>Over recent years, climate change has become a global issue, leading political agendas and projecting onto almost every economic and development decision made today.  However, the way that we conduct climate science has remained broadly unchanged since the publication of the first IPCC report in 1990 - still relying on an ensemble of opportunity of climate models which doesn't allow for an estimation of high-impact tail risks and a highly idealized scenario framework which fails to test the fundamental technological assumptions which underpin our remaining pathways for achieving the Paris Agreement.  Here, we discuss how our strategy within the Make Our Planet Great Again "RISCCi" project is attempting to reframe the simulation of climate projections such as to provide better guidance for robust decision-making by categorizing the deep uncertainties of climate projections and mitigation pathways.  We present the initial results from an CNRM ensemble project which seeks to explore tail behaviour in climate feedbacks and impacts, and outline in a wider sense how future work and climate assessment needs to respond to the growing and evolving needs of a society as it works to minimise, and adapt to, climate change.</p>

Climate Change over the Equatorial Indo-Pacific in Global Warming*

2009 ◽  
Vol 22 (10) ◽  
pp. 2678-2693 ◽  
Author(s):  
Chie Ihara ◽  
Yochanan Kushnir ◽  
Mark A. Cane ◽  
Victor H. de la Peña
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Indian Ocean ◽  
Wind Stress ◽  
Zonal Wind ◽  
Climate Models ◽  
Emission Scenario ◽  
Equatorial Indian Ocean ◽  
Zonal Wind Stress ◽  
Sst Gradient

Abstract The response of the equatorial Indian Ocean climate to global warming is investigated using model outputs submitted to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. In all of the analyzed climate models, the SSTs in the western equatorial Indian Ocean warm more than the SSTs in the eastern equatorial Indian Ocean under global warming; the mean SST gradient across the equatorial Indian Ocean is anomalously positive to the west in a warmer twenty-first-century climate compared to the twentieth-century climate, and it is dynamically consistent with the anomalous westward zonal wind stress and anomalous positive zonal sea level pressure (SLP) gradient to the east at the equator. This change in the zonal SST gradient in the equatorial Indian Ocean is detected even in the lowest-emission scenario, and the size of the change is not necessarily larger in the higher-emission scenario. With respect to the change over the equatorial Pacific in climate projections, the subsurface central Pacific displays the strongest cooling or weakest warming around the thermocline depth compared to that above and below in all of the climate models, whereas changes in the zonal SST gradient and zonal wind stress around the equator are model dependent and not straightforward.

scholarly journals Emergent Constraints on Climate-Carbon Cycle Feedbacks

2019 ◽  
Vol 5 (4) ◽  
pp. 275-281 ◽  
Author(s):  
Peter M. Cox
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Projections ◽  
Earth System ◽  
Model Errors ◽  
The Earth ◽  
The Future ◽  
Emergent Constraints

Abstract Purpose of Review Feedbacks between CO2-induced climate change and the carbon cycle are now routinely represented in the Earth System Models (ESMs) that are used to make projections of future climate change. The inconclusion of climate-carbon cycle feedbacks in climate projections is an important advance, but has added a significant new source of uncertainty. This review assesses the potential for emergent constraints to reduce the uncertainties associated with climate-carbon cycle feedbacks. Recent Findings The emergent constraint technique involves using the full ensemble of models to find an across-ensemble relationship between an observable feature of the Earth System (such as a trend, interannual variation or change in seasonality) and an uncertain aspect of the future. Examples focussing on reducing uncertainties in future atmospheric CO2 concentration, carbon loss from tropical land under warming and CO2 fertilization of mid- and high-latitude photosynthesis are exemplars of these different types of emergent constraints. Summary The power of emergent constraints is that they use the enduring range in model projections to reduce uncertainty in the future of the real Earth System, but there are also risks that indiscriminate data-mining, and systematic model errors could yield misleading constraints. A hypothesis-driven theory-led approach can overcome these risks and also reveal the true promise of emergent constraints—not just as ways to reduce uncertainty in future climate change but also to catalyse advances in our understanding of the Earth System.

scholarly journals Dry Spells in Croatia: Observed Climate Change and Climate Projections

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 652
Author(s):  
Ivana Marinović ◽  
Ksenija Cindrić Kalin ◽  
Ivan Güttler ◽  
Zoran Pasarić
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Daily Precipitation ◽  
Global Climate Models ◽  
Climate Change Signal ◽  
Climate Projections ◽  
Model Errors ◽  
Dry Spells ◽  
Daily Precipitation Data ◽  
Increasing Trend

This study performs a systematic analysis of the recent and future changes of dry spells (DS) in Croatia. DS are defined as consecutive sequences of days with daily precipitation less than 5 mm of the precipitation-per-day threshold (DS5). Daily precipitation data come from a dense national rain gauge network (covering seven regions) and span the period 1961–2015. The spatial and temporal changes of the observed mean (MDS5) and maximum (MxDS5) seasonal and annual dry spells were analysed by means of the Kendall tau method and the partial trend method. Future changes of DS5 were assessed by employing the three regional climate models (RegCM4, CCLM4 and RCA4) covering the EURO-CORDEX domain with a 12.5-km horizontal resolution, resulting in a realistic orography and land–sea border over Croatia. The models were forced at their boundaries by the four CMIP5 global climate models. For the reference period 1971–2000, the observed, as well as modelled, DS5 were analysed, and the systematic model errors were assessed. Finally, the projections and future changes of the DS5 statistics based on simulations under the high and medium greenhouse gases concentration scenarios (i.e., RCP8.5 and RCP4.5) with a focus on the climate change signal between 1971–2000 and two future periods, 2011–2040 and 2041–2070, were examined. A prevailing increasing trend of MDS5 was found in the warm part of the year, being significant in the mountainous littoral and North Adriatic coastal region. An increasing trend of MxDS5 was also found in the warm part of the year (both the spring and summer), and it was particularly pronounced along the Adriatic coast, while a coherent negative trend pattern was found in the autumn. By applying the partial trend methodology, an increase was found in the very long DS5 (above the 90th percentile) in the recent half of the analysed 55-year period in all seasons, except in the autumn when shortening in the DS5 was detected. The climate change signal during the two analysed future periods was positive for the summer in all regions, weakly negative for the winter and not conclusive for the spring, autumn and year. It was found that no RCM-GCM combination is the best in all cases, since the most successful model combinations depend on the season and location.

Underestimation of the projected 21st century increase in drought duration, according to emergent constraint: consistent result in CMIP5 and CMIP6 models

2021 ◽  
Author(s):  
Irina Y. Petrova ◽  
Diego G. Miralles ◽  
Florent Brient ◽  
Markus Donat
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Climate Models ◽  
Model Errors ◽  
Drought Duration ◽  
Intergovernmental Panel ◽  
Uncertainty Range ◽  
The Future ◽  
Emergent Constraint ◽  
Global Mean

<p>Droughts are defined as one of the most devastating natural disasters of modern times and a key challenge faced under climate change. The complexity of interacting physical processes that underlie the shortage of rainfall in climate models hampers accurate representation of present-day droughts, and leads to differences in their responses to increased greenhouse gas (GHG) concentrations in the future. As a result, the confidence in drought projections is currently defined as ‘medium to low' by the Intergovernmental Panel on Climate Change (IPCC), and reducing this uncertainty remains one of the main goals in coming years, with significant benefits for human and natural systems. </p><p>In this study we explore a relationship between biases in simulated present-day values of longest annual drought (LAD) and future projections of LAD in an ensemble of CMIP5 and CMIP6 models. We find that present-day model bias explains almost 95 % of the future uncertainty in LAD by the end of the 21st century, attributed to the well-known precipitation simulation errors: “drier” models with longer annual droughts at present tend to predict larger LAD values worldwide in the future, as well as a stronger response to GHG forcing in LAD, which is significant in more than 40 % of the global land area.</p><p>Substituting observational LAD estimates from satellite data into this model-revealed “present–future relationlarship” suggests that the 21st century global mean increase in duration of annual meteorological droughts could be significantly larger than predicted by the CMIP5 and CMIP6 model ensembles. This emergent constraint reduces global mean uncertainty range in future LAD estimates from 45–100 to 75–90 days, a level more typical of the prediction range of “drier” models. The findings reveal world regions where climate change may cause stronger meteorological drought aggravation than expected, and emphasise the importance of reducing model errors, which are presently largely owed to rain biases, to increase confidence in future predictions.</p>

scholarly journals The effects of ENSO, climate change and human activities on the water level of Lake Toba, Indonesia: a critical literature review

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hendri Irwandi ◽  
Mohammad Syamsu Rosid ◽  
Terry Mart
Keyword(s):  
Climate Change ◽  
Water Level ◽  
Human Activities ◽  
Southern Oscillation ◽  
Climatic Conditions ◽  
Water Levels ◽  
Dominant Factor ◽  
Climate Projections ◽  
Continuous Increase ◽  
The Future

AbstractThis research quantitatively and qualitatively analyzes the factors responsible for the water level variations in Lake Toba, North Sumatra Province, Indonesia. According to several studies carried out from 1993 to 2020, changes in the water level were associated with climate variability, climate change, and human activities. Furthermore, these studies stated that reduced rainfall during the rainy season due to the El Niño Southern Oscillation (ENSO) and the continuous increase in the maximum and average temperatures were some of the effects of climate change in the Lake Toba catchment area. Additionally, human interventions such as industrial activities, population growth, and damage to the surrounding environment of the Lake Toba watershed had significant impacts in terms of decreasing the water level. However, these studies were unable to determine the factor that had the most significant effect, although studies on other lakes worldwide have shown these factors are the main causes of fluctuations or decreases in water levels. A simulation study of Lake Toba's water balance showed the possibility of having a water surplus until the mid-twenty-first century. The input discharge was predicted to be greater than the output; therefore, Lake Toba could be optimized without affecting the future water level. However, the climate projections depicted a different situation, with scenarios predicting the possibility of extreme climate anomalies, demonstrating drier climatic conditions in the future. This review concludes that it is necessary to conduct an in-depth, comprehensive, and systematic study to identify the most dominant factor among the three that is causing the decrease in the Lake Toba water level and to describe the future projected water level.

scholarly journals Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part II: Climate Change Impact Assessment)

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1548
Author(s):  
Suresh Marahatta ◽  
Deepak Aryal ◽  
Laxmi Prasad Devkota ◽  
Utsav Bhattarai ◽  
Dibesh Shrestha
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Assessment Tool ◽  
Swat Model ◽  
Global Climate Models ◽  
Climate Data ◽  
The Future ◽  
The Impact ◽  
Mountainous River

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.

Realised added value in dynamical downscaling of Australian climate change

2021 ◽  
Author(s):  
Giovanni Di Virgilio ◽  
Jason P. Evans ◽  
Alejandro Di Luca ◽  
Michael R. Grose ◽  
Vanessa Round ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Adaptation ◽  
Regional Climate ◽  
Future Climate ◽  
Added Value ◽  
Climate Projections ◽  
Eastern Australia ◽  
Australian Alps ◽  
Future Climate Projections

<p>Coarse resolution global climate models (GCM) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. However, evidence of where and when downscaling provides new information about both the current climate (added value, AV) and projected climate change signals, relative to driving data, is lacking. Seasons and locations where CORDEX-Australasia ERA-Interim and GCM-driven RCMs show AV for mean and extreme precipitation and temperature are identified. A new concept is introduced, ‘realised added value’, that identifies where and when RCMs simultaneously add value in the present climate and project a different climate change signal, thus suggesting plausible improvements in future climate projections by RCMs. ERA-Interim-driven RCMs add value to the simulation of summer-time mean precipitation, especially over northern and eastern Australia. GCM-driven RCMs show AV for precipitation over complex orography in south-eastern Australia during winter and widespread AV for mean and extreme minimum temperature during both seasons, especially over coastal and high-altitude areas. RCM projections of decreased winter rainfall over the Australian Alps and decreased summer rainfall over northern Australia are collocated with notable realised added value. Realised added value averaged across models, variables, seasons and statistics is evident across the majority of Australia and shows where plausible improvements in future climate projections are conferred by RCMs. This assessment of varying RCM capabilities to provide realised added value to GCM projections can be applied globally to inform climate adaptation and model development.</p>

scholarly journals Simulating future precipitation extremes in a complex Alpine catchment

2013 ◽  
Vol 13 (2) ◽  
pp. 263-277 ◽  
Author(s):  
C. Dobler ◽  
G. Bürger ◽  
J. Stötter
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Precipitation Extremes ◽  
Climate Projections ◽  
Research Station ◽  
Climate Change Scenarios ◽  
Station Data

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

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