scholarly journals Multimodel Multisignal Climate Change Detection at Regional Scale

2006 ◽  
Vol 19 (17) ◽  
pp. 4294-4307 ◽  
Author(s):  
Xuebin Zhang ◽  
Francis W. Zwiers ◽  
P. A. Stott
Keyword(s):  
Climate Change ◽  
Twentieth Century ◽  
Greenhouse Gases ◽  
Regional Scale ◽  
Internal Variability ◽  
Anthropogenic Forcing ◽  
Internal Climate Variability ◽  
Aerosol Forcing ◽  
Spatial Domains ◽  
Northern Hemispheric

Abstract Using an optimal detection technique and climate change simulations produced with two versions of two GCMs, we have assessed the causes of twentieth-century temperature changes from global to regional scales. Our analysis is conducted in nine spatial domains: 1) the globe; 2) the Northern Hemisphere; four large regions in the Northern Hemispheric midlatitudes covering 30°–70°N including 3) Eurasia, 4) North America, 5) Northern Hemispheric land only, 6) the entire 30°–70°N belt; and three smaller regions over 7) southern Canada, 8) southern Europe, and 9) China. We find that the effect of anthropogenic forcing on climate is clearly detectable at global through regional scales. The effect of combined greenhouse gases and sulfate aerosol forcing is detectable in all nine domains in annual and seasonal mean temperatures observed during the second half of the twentieth century. The effect of greenhouse gases can also be separated from that of sulfate aerosols over this period at continental and regional scales. Uncertainty in these results is larger in the smaller spatial domains. Detection is improved when an ensemble of models is used to estimate the response to anthropogenic forcing and the underlying internal variability of the climate system. Our detection results hold after removal of North Atlantic Oscillation (NAO)-related variability in temperature observations—variability that may or may not be associated with anthropogenic forcing. They also continue to hold when our estimates of natural internal climate variability are doubled.

A Bayesian Assessment of Climate Change Using Multimodel Ensembles. Part II: Regional and Seasonal Mean Surface Temperatures

2007 ◽  
Vol 20 (12) ◽  
pp. 2769-2790 ◽  
Author(s):  
Seung-Ki Min ◽  
Andreas Hense
Keyword(s):  
Climate Change ◽  
Twentieth Century ◽  
South America ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Bayesian Decision ◽  
Intergovernmental Panel ◽  
Anthropogenic Forcing ◽  
Model Ensembles ◽  
Multimodel Ensembles

Abstract A Bayesian approach is applied to the observed regional and seasonal surface air temperature (SAT) changes using single-model ensembles (SMEs) with the ECHO-G model and multimodel ensembles (MMEs) of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) simulations. Bayesian decision classifies observations into the most probable scenario out of six available scenarios: control (CTL), natural forcing (N), anthropogenic forcing (ANTHRO), greenhouse gas (G), sulfate aerosols (S), and natural plus anthropogenic forcing (ALL). Space–time vectors of the detection variable are constructed for six continental regions (North America, South America, Asia, Africa, Australia, and Europe) by combining temporal components of SATs (expressed as Legendre coefficients) from two or three subregions of each continental region. Bayesian decision results show that over most of the regions observed SATs are classified into ALL or ANTHRO scenarios for the whole twentieth century and its second half. Natural forcing and ALL scenarios are decided during the first half of the twentieth century, but only in the low-latitude region (Africa and South America), which might be related to response patterns to solar forcing. Overall seasonal decisions follow annual results, but there are notable seasonal dependences that differ between regions. A comparison of SME and MME results demonstrates that the Bayesian decisions for regional-scale SATs are largely robust to intermodel uncertainties as well as prior probability and temporal scales, as found in the global results.

scholarly journals Regional aspects of global climate change simulations : Validation and assessment of climate response over Indian monsoon region to transient increase of greenhouse gases and sulfate aerosols

MAUSAM ◽  
2021 ◽  
Vol 52 (1) ◽  
pp. 229-244
Author(s):  
K. RUPA KUMAR ◽  
R. G. ASHRIT
Keyword(s):  
Climate Change ◽  
Greenhouse Gases ◽  
General Circulation ◽  
Regional Scale ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Change Scenario ◽  
Climate Projections ◽  
Sulfate Aerosols ◽  
Monsoon Climate

The regional climatic impacts associated with global climatic change and their assessment are very important since agriculture, water resources, ecology etc., are all vulnerable to climatic changes on regional scale. Coupled Atmosphere-Ocean general circulation model (AOGCM) simulations provide a range of scenarios, which can be used, for the assessment of impacts and development of adaptive or mitigative strategies. Validation of the models against the observations and establishing the sensitivity to climate change forcing are essential before the model projections are used for assessment of possible impacts. Moreover model simulated climate projections are often of coarse resolution while the models used for impact assessment, (e.g. crop simulation models, or river runoff models etc.) operate on a higher spatial resolution. This spatial mismatch can be overcome by adopting an appropriate strategy of downscaling the GCM output.   This study examines two AOGCM (ECHAM4/OPYC3 and HadCM2) climate change simulations for their performance in the simulation of monsoon climate over India and the sensitivity of the simulated monsoon climate to transient changes in the atmospheric concentrations of greenhouse gases and sulfate aerosols. The results show that the two models simulate the gross features of climate over India reasonably well. However the inter-model differences in simulation of mean characteristics, sensitivity to forcing and in the simulation of climate change suggest need for caution. Further an empirical downscaling approach in used to assess the possibility of using GCM projections for preparation of regional climate change scenario for India.

scholarly journals Optimal Detection of Regional Trends Using Global Data

2010 ◽  
Vol 23 (16) ◽  
pp. 4438-4446 ◽  
Author(s):  
Stephen S. Leroy ◽  
James G. Anderson
Keyword(s):  
Climate Change ◽  
Model Uncertainty ◽  
Climate Models ◽  
Region Of Interest ◽  
Internal Variability ◽  
Spatiotemporal Patterns ◽  
Optimal Detection ◽  
Internal Climate Variability ◽  
Climate Signals ◽  
Normally Distributed

Abstract A complete accounting of model uncertainty in the optimal detection of climate signals requires normalization of the signals produced by climate models; however, there is not yet a well-defined rule for the normalization. This study seeks to discover such a rule. The authors find that, to arrive at the equations of optimal detection from a general application of Bayesian statistics to the problem of climate change, it is necessary to assume that 1) the prior probability density function (PDF) for climate change is separable into independent PDFs for sensitivity and the signals’ spatiotemporal patterns; 2) postfit residuals are due to internal variability and are normally distributed; 3) the prior PDF for sensitivity is uninformative; and 4) a continuum of climate models used to estimate model uncertainty gives a normally distributed PDF for the spatiotemporal patterns for the climate signals. This study also finds that the rule for normalization of the signals’ patterns is a simple division of model-simulated climate change in any observable quantity or set of quantities by a change in a single quantity of interest such as regionally averaged temperature or precipitation. With this normalization, optimal detection yields the most probable estimates of the underlying changes in the region of interest due to external forcings. Data outside the region of interest add information that effectively suppresses the interannual fluctuations associated with internal climate variability.

scholarly journals Robust detection of forced warming in the presence of potentially large climate variability

2021 ◽  
Author(s):  
Sebastian Sippel ◽  
Nicolai Meinshausen ◽  
Eniko Székely ◽  
Erich Fischer ◽  
Angeline G. Pendergrass ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Climate Models ◽  
Climate Model ◽  
Decadal Variability ◽  
Internal Variability ◽  
Warming Trend ◽  
Robust Detection ◽  
Internal Climate Variability ◽  
Detection And Attribution

<p>Warming of the climate system is unequivocal and substantially exceeds unforced internal climate variability. Detection and attribution (D&A) employs spatio-temporal fingerprints of the externally forced climate response to assess the magnitude of a climate signal, such as the multi-decadal global temperature trend, while internal variability is often estimated from unforced (“control”) segments of climate model simulations (e.g. Santer et al. 2019). Estimates of the exact magnitude of decadal-scale internal variability, however, remain uncertain and are limited by relatively short observed records, their entanglement with the forced response, and considerable spread of simulated variability across climate models. Hence, a limitation of D&A is that robustness and confidence levels depend on the ability of climate models to correctly simulate internal variability (Bindoff et al., 2013).</p><p>For example, the large spread in simulated internal variability across climate models implies that the observed 40-year global mean temperature trend of about 0.76°C (1980-2019) would exceed the standard deviation of internally generated variability of a set of `low variability' models by far (> 5σ), corresponding to vanishingly small probabilities if taken at face value. But the observed trend would exceed the standard deviation of a few `high-variability' climate models `only' by a factor of about two, thus unlikely to be internally generated but not practically impossible given unavoidable climate system and observational uncertainties. This illustrates the key role of model uncertainty in the simulation of internal variability for D&A confidence estimates.</p><p>Here we use a novel statistical learning method to extract a fingerprint of climate change that is robust towards model differences and internal variability, even of large amplitude. We demonstrate that externally forced warming is distinct from internal variability and detectable with high confidence on any state-of-the-art climate model, even those that simulate the largest magnitude of unforced multi-decadal variability. Based on the median of all models, it is extremely likely that more than 85% of the observed warming trend over the last 40 years is externally driven. Detection remains robust even if their main modes of decadal variability would be scaled by a factor of two. It is extremely likely that at least 55% of the observed warming trend over the last 40 years cannot be explained by internal variability irrespective of which climate model’s natural variability estimates are used.</p><p>Our analysis helps to address this limitation in attributing warming to external forcing and provides a novel perspective for quantifying the magnitude of forced climate change even under uncertain but potentially large multi-decadal internal climate variability. This opens new opportunities to make D&A fingerprints robust in the presence of poorly quantified yet important features inextricably linked to model structural uncertainty, and the methodology may contribute to more robust detection and attribution of climate change to its various drivers.</p><p> </p><p>Bindoff, N.L., et al., 2013. Detection and attribution of climate change: from global to regional. IPCC AR5, WG1, Chapter 10.</p><p>Santer, B.D., et al., 2019. Celebrating the anniversary of three key events in climate change science. <em>Nat Clim Change</em> <strong>9</strong>(3), pp. 180-182.</p>

scholarly journals A Limited Role for Unforced Internal Variability in Twentieth-Century Warming

2019 ◽  
Vol 32 (16) ◽  
pp. 4893-4917 ◽  
Author(s):  
Karsten Haustein ◽  
Friederike E. L. Otto ◽  
Victor Venema ◽  
Peter Jacobs ◽  
Kevin Cowtan ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Surface Temperature ◽  
Radiative Forcing ◽  
Low Frequency ◽  
Internal Variability ◽  
External Forcing ◽  
Anthropogenic Aerosol ◽  
Transient Climate Response ◽  
The North ◽  
Aerosol Forcing

AbstractThe early twentieth-century warming (EW; 1910–45) and the mid-twentieth-century cooling (MC; 1950–80) have been linked to both internal variability of the climate system and changes in external radiative forcing. The degree to which either of the two factors contributed to EW and MC, or both, is still debated. Using a two-box impulse response model, we demonstrate that multidecadal ocean variability was unlikely to be the driver of observed changes in global mean surface temperature (GMST) after AD 1850. Instead, virtually all (97%–98%) of the global low-frequency variability (>30 years) can be explained by external forcing. We find similarly high percentages of explained variance for interhemispheric and land–ocean temperature evolution. Three key aspects are identified that underpin the conclusion of this new study: inhomogeneous anthropogenic aerosol forcing (AER), biases in the instrumental sea surface temperature (SST) datasets, and inadequate representation of the response to varying forcing factors. Once the spatially heterogeneous nature of AER is accounted for, the MC period is reconcilable with external drivers. SST biases and imprecise forcing responses explain the putative disagreement between models and observations during the EW period. As a consequence, Atlantic multidecadal variability (AMV) is found to be primarily controlled by external forcing too. Future attribution studies should account for these important factors when discriminating between externally forced and internally generated influences on climate. We argue that AMV must not be used as a regressor and suggest a revised AMV index instead [the North Atlantic Variability Index (NAVI)]. Our associated best estimate for the transient climate response (TCR) is 1.57 K (±0.70 at the 5%–95% confidence level).

scholarly journals Detectability of Changes in the Walker Circulation in Response to Global Warming*

2013 ◽  
Vol 26 (12) ◽  
pp. 4038-4048 ◽  
Author(s):  
Pedro N. DiNezio ◽  
Gabriel A. Vecchi ◽  
Amy C. Clement
Keyword(s):  
Greenhouse Gases ◽  
Numerical Experiments ◽  
System Analysis ◽  
Climate Models ◽  
Coupled Model ◽  
Walker Circulation ◽  
Internal Variability ◽  
Radiative Forcings ◽  
Aerosol Forcing ◽  
The Walker Circulation

Abstract Changes in the gradients in sea level pressure (SLP) and sea surface temperature (SST) along the equatorial Pacific are analyzed in observations and 101 numerical experiments performed with 37 climate models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The ensemble of numerical experiments simulates changes in the earth’s climate during the 1870–2004 period in response to changes in natural (solar variations and volcanoes) and anthropogenic (well-mixed greenhouse gases, ozone, direct aerosol forcing, and land use) radiative forcings. A reduction in the zonal SLP gradient is present in observational records and is the typical response of the ensemble, yet only 26 out of the 101 experiments exhibit a reduced SLP gradient within 95% statistical confidence of the observed value. The multimodel response indicates a reduction of the Walker circulation to historical forcings, albeit an order of magnitude smaller than the observed value. There are multiple nonexclusive interpretations of these results: (i) the observed trend may not be entirely forced and includes a substantial component from internal variability; (ii) there are problems with the observational record that lead to a spuriously large trend; and (iii) the strength of the Walker circulation, as measured by the zonal SLP gradient, may be less sensitive to external forcing in models than in the real climate system. Analysis of a subset of experiments suggests that greenhouse gases act to weaken the circulation, but aerosol forcing drives a strengthening of the circulation, which appears to be overestimated by the models, resulting in a muted response to the combined anthropogenic forcings.

El Niño variability mediates 21st century growth effects of climate change

2021 ◽  
Author(s):  
Christopher Callahan ◽  
Justin Mankin
Keyword(s):  
Climate Change ◽  
Economic Growth ◽  
Climate Variability ◽  
Climate Model ◽  
Internal Variability ◽  
Climate Impact ◽  
Growth Effects ◽  
Internal Climate Variability ◽  
Mitigation And Adaptation ◽  
Model Ensembles

<p>Understanding the effect of climate change on global economic growth is critical to informing optimal mitigation and adaptation policy. Many recent efforts have been made to empirically quantify the roles of weather and climate in economic growth, but these efforts have generally focused on changes in mean climate rather than changes in climate variability. Climate change is expected to alter modes of climate variability, so fully quantifying the costs of climate change requires both understanding the effects of climate variability on economic growth and constraining how this variability will evolve under forcing. Here we combine historical climate and economic data with multiple climate model ensembles to quantify the economic growth effects of El Niño and examine how these effects evolve in the 21<sup>st</sup> century. Preliminary results show substantial negative effects of El Niño on growth, with historical events reducing growth by >5 percentage points over 5 years in countries whose temperature variability is tightly correlated with ENSO. We then examine how climate change influences El Niño and its growth effects in both multi-model and single-model ensembles, allowing us to isolate the role of internal climate variability in shaping the evolution of ENSO statistics in the 21<sup>st</sup> century. Climate change is generally projected to increase El Niño frequency and thus the resulting growth penalties, but internal variability generates a wide spread of responses, all of which are consistent with the same forcing. These results highlight how internal variability can influence both interannual El Niño occurrence and long-term changes in its statistics, with consequences for future economic growth. Moreover, these results illustrate the range of climate impact trajectories that are consistent with the same emissions, providing critical information for adaptation decision-makers needing to construct robust socioeconomic systems in the face of 21<sup>st</sup> century climate change.</p>

scholarly journals Applicability of ensemble pattern scaling method on precipitation intensity indices at regional scale

2011 ◽  
Vol 8 (3) ◽  
pp. 5227-5261 ◽  
Author(s):  
Y. Li ◽  
W. Ye
Keyword(s):  
Climate Change ◽  
Regional Scale ◽  
National Level ◽  
Internal Variability ◽  
Future Climate ◽  
Precipitation Intensity ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Scaling Method ◽  
Model Variability

Abstract. Pattern scaling constructs future climate change scenarios using the normalized change patterns of GCMs, offers the possibility of representing the whole range of uncertainties involved in future climate change projection. This paper investigates the applicability and uncertainty associated with the pattern scaling method in constructing the changes of future precipitation intensity indices at regional scale, using a two-step ensemble approach. In the first step, the linearity accuracy and GCM internal variability were examined explicitly. The inter-model variability of the GCMs and associated confidence intervals were produced in the second step ensemble. Australia and its 7 administrative regions was selected as the study area and three precipitation intensity indices, including two precipitation extreme indices, were used for the examination: i.e., the 99th percentile daily precipitation intensity (P99), the 20-yr-return extreme precipitation intensity (RP20), and the mean precipitation intensity (precipitation amount per wet day) (RPD). A total of 12 IPCC AR4 GCMs with 6 simulation samples were used for the ensemble. For the 3 precipitation intensity indices, good linear relationships between precipitation intensity indices change and global mean temperature change at the national level were found for most GCMs, however, the linear relationship weakened when the analysis was applied to the administrative regions. In addition, the GCM internal signal-to-noise ratios for each GCM tended to decrease at the regional and grid cell levels, along with the reduction in spatial scale. Both GCM-internal and inter-model variability was significant, and the inter-model variability was larger than GCM-internal variability. The final result of the inter-model ensemble median results show that for Australia, in general, all three indices will increase under global warming, with the change rates being 3.56, 7.62 and 2.26 % K−1 for P99, RP20 and RPD respectively at the national level.

scholarly journals Investigating the Influence of Anthropogenic Forcing on Observed Mean and Extreme Sea Level Pressure Trends over the Mediterranean Region

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Armineh Barkhordarian
Keyword(s):  
Sea Level ◽  
Mediterranean Region ◽  
Large Scale ◽  
Internal Variability ◽  
Small Scale ◽  
Sea Level Pressure ◽  
Anthropogenic Forcing ◽  
Level Pressure ◽  
Internal Climate Variability ◽  
The Mediterranean

We investigate whether the observed mean sea level pressure (SLP) trends over the Mediterranean region in the period from 1975 to 2004 are significantly consistent with what 17 models projected as response of SLP to anthropogenic forcing (greenhouse gases and sulphate aerosols, GS). Obtained results indicate that the observed trends in mean SLP cannot be explained by natural (internal) variability. Externally forced changes are detectable in all seasons, except spring. The large-scale component (spatial mean) of the GS signal is detectable in all the 17 models in winter and in 12 of the 17 models in summer. However, the small-scale component (spatial anomalies about the spatial mean) of GS signal is only detectable in winter within 11 of the 17 models. We also show that GS signal has a detectable influence on observed decreasing (increasing) tendency in the frequencies of extremely low (high) SLP days in winter and that these changes cannot be explained by internal climate variability. While the detection of GS forcing is robust in winter and summer, there are striking inconsistencies in autumn, where analysis points to the presence of an external forcing, which is not GS forcing.

Sign in / Sign up

Export Citation Format

Share Document