scholarly journals Forced and Unforced Decadal Behavior of the Interhemispheric SST Contrast during the Instrumental Period (1881–2012): Contextualizing the Late 1960s–Early 1970s Shift

2020 ◽  
Vol 33 (9) ◽  
pp. 3487-3509 ◽  
Author(s):  
Andrew R. Friedman ◽  
Gabriele C. Hegerl ◽  
Andrew P. Schurer ◽  
Shih-Yu Lee ◽  
Wenwen Kong ◽  
...  
Keyword(s):  
Coupled Model ◽  
Internal Variability ◽  
Forced Response ◽  
Meridional Overturning Circulation ◽  
Large Magnitude ◽  
Sst Cooling ◽  
Sst Variability ◽  
External Forcings ◽  
Sst Warming ◽  
Instrumental Period

AbstractThe sea surface temperature (SST) contrast between the Northern Hemisphere (NH) and Southern Hemisphere (SH) influences the location of the intertropical convergence zone (ITCZ) and the intensity of the monsoon systems. This study examines the contributions of external forcing and unforced internal variability to the interhemispheric SST contrast in HadSST3 and ERSSTv5 observations, and 10 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) from 1881 to 2012. Using multimodel mean fingerprints, a significant influence of anthropogenic, but not natural, forcing is detected in the interhemispheric SST contrast, with the observed response larger than that of the model mean in ERSSTv5. The forced response consists of asymmetric NH–SH SST cooling from the mid-twentieth century to around 1980, followed by opposite NH–SH SST warming. The remaining best-estimate residual or unforced component is marked by NH–SH SST maxima in the 1930s and mid-1960s, and a rapid NH–SH SST decrease around 1970. Examination of decadal shifts in the observed interhemispheric SST contrast highlights the shift around 1970 as the most prominent from 1881 to 2012. Both NH and SH SST variability contributed to the shift, which appears not to be attributable to external forcings. Most models examined fail to capture such large-magnitude shifts in their control simulations, although some models with high interhemispheric SST variability are able to produce them. Large-magnitude shifts produced by the control simulations feature disparate spatial SST patterns, some of which are consistent with changes typically associated with the Atlantic meridional overturning circulation (AMOC).

scholarly journals Scant evidence for a volcanically forced winter warming over Eurasia following the Krakatau eruption of August 1883

2020 ◽  
Vol 20 (22) ◽  
pp. 13687-13700
Author(s):  
Lorenzo M. Polvani ◽  
Suzana J. Camargo
Keyword(s):  
Coupled Model ◽  
Large Magnitude ◽  
Low Latitude ◽  
Surface Warming ◽  
Winter Warming ◽  
Pinatubo Eruption ◽  
Intercomparison Project ◽  
Volcanic Aerosols ◽  
Instrumental Period ◽  
New Evidence

Abstract. A recent study has presented compelling new evidence suggesting that the observed Eurasian warming in the winter following the 1992 Pinatubo eruption was, in all likelihood, unrelated to the presence of volcanic aerosols in the stratosphere. Building on that study, we turn our attention to the only other low-latitude eruption in the instrumental period with a comparably large magnitude: the Krakatau eruption of August 1883. We study the temperature anomalies in the first winter following that eruption in detail, analyzing (1) observations, (2) reanalyses, and (3) models. Three findings emerge from our analysis. First, the observed post-Krakatau winter warming over Eurasia was unremarkable (only between 1σ and 2σ of the distribution from 1850 to present). Second, reanalyses based on assimilating surface pressure alone indicate the existence of very large uncertainties, so much so that a Eurasian cooling is not incompatible with those reanalyses. Third, models robustly show the complete absence of a volcanically forced Eurasian winter warming: here, we analyze both a 100-member initial-condition ensemble and 140 simulations from Phase 5 of the Coupled Model Intercomparison Project. This wealth of evidence strongly suggests that, as in the case of Pinatubo, the observed warming over Eurasia in the winter of 1883–84 was, in all likelihood, unrelated to the Krakatau eruption. This, taken together with a similar result for Pinatubo, leads us to conclude that if volcanically forced Eurasian winter warming exists at all, an eruption with a magnitude far exceeding these two events would be needed to produce a detectable surface warming.

scholarly journals Description and evaluation of NorESM1-F: A fast version of the Norwegian Earth System Model (NorESM)

2018 ◽  
Author(s):  
Chuncheng Guo ◽  
Mats Bentsen ◽  
Ingo Bethke ◽  
Mehmet Ilicak ◽  
Jerry Tjiputra ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Sea Ice ◽  
Large Scale ◽  
Coupled Model ◽  
Coupled System ◽  
Internal Variability ◽  
Earth System Model ◽  
Meridional Overturning Circulation ◽  
System Model ◽  
Earth System

Abstract. A new computationally efficient version of the Norwegian Earth System Model (NorESM) is presented. This new version (here termed NorESM1-F) runs about 2.5 times faster (e.g. 90 model years per day on current hardware) than the version that contributed to the fifth phase of the Coupled Model Intercomparison project (CMIP5), i.e., NorESM1-M, and is therefore particularly suitable for multi-millennial paleoclimate and carbon cycle simulations or large ensemble simulations. The speedup is primarily a result of using a prescribed atmosphere aerosol chemistry and a tripolar ocean-sea ice horizontal grid configuration that allows an increase of the ocean-sea ice component time steps. Ocean biogeochemistry can be activated for fully coupled and semi-coupled carbon cycle applications. This paper describes the model and evaluates its performance using observations and NorESM1-M as benchmarks. The evaluation emphasises model stability, important large-scale features in the ocean and sea ice components, internal variability in the coupled system, and climate sensitivity. Simulation results from NorESM1-F in general agree well with observational estimates, and show evident improvements over NorESM1-M, for example, in the strength of the meridional overturning circulation and sea ice simulation, both important metrics in simulating past and future climates. Whereas NorESM1-M showed a slight global cool bias in the upper oceans, NorESM1-F exhibits a global warm bias. In general, however, NorESM1-F has more similarities than dissimilarities compared to NorESM1-M, and some biases and deficiencies known in NorESM1-M remain.

Forced modulations of Sahel rainfall at decadal timescale over the 20th Century.

2021 ◽  
Author(s):  
Cassien Diabe Ndiaye ◽  
Juliette Mignot ◽  
Elsa Mohino
Keyword(s):  
North Atlantic Ocean ◽  
Coupled Model ◽  
Internal Variability ◽  
Boreal Summer ◽  
Semiarid Region ◽  
Rainfall Regime ◽  
Anthropogenic Aerosols ◽  
Coupled Models ◽  
External Forcings ◽  
Sahel Precipitation

<p>The semiarid region of the Sahel was marked during the 20<sup>th</sup> Century by significant modulations of its rainfall regime. Part of these modulations has been associated with the internal variability of the climate system, mediated by changes in oceanic sea surface temperature (SST). We show here that the external forcings, and in particular anthropogenic aerosols, might have played a role more important than previously thought in setting these variations. The study is based on the recent simulations performed for CMIP6 with the IPSL-CM6A-LR coupled model. As in most coupled models, the maximum precipitation is limited to the southern Sahel during boreal summer in the IPSL-CM6A-LR model. A novel definition of the Sahel precipitation region is proposed in order to take this bias into account. Our results show that external forcings induce decadal modulations of Sahel precipitation that correlate significantly at 0.6 with the observed precipitations and that the anthropogenic aerosols explain more than 70% of these modulations. These results confirm recent results of CMIP6 highlighting an important role of aerosol forcing for the decadal climate in and around the North Atlantic ocean.</p>

scholarly journals Description and evaluation of NorESM1-F: a fast version of the Norwegian Earth System Model (NorESM)

2019 ◽  
Vol 12 (1) ◽  
pp. 343-362 ◽  
Author(s):  
Chuncheng Guo ◽  
Mats Bentsen ◽  
Ingo Bethke ◽  
Mehmet Ilicak ◽  
Jerry Tjiputra ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Sea Ice ◽  
Large Scale ◽  
Coupled Model ◽  
Coupled System ◽  
Internal Variability ◽  
Earth System Model ◽  
Meridional Overturning Circulation ◽  
System Model ◽  
Earth System

Abstract. A new computationally efficient version of the Norwegian Earth System Model (NorESM) is presented. This new version (here termed NorESM1-F) runs about 2.5 times faster (e.g., 90 model years per day on current hardware) than the version that contributed to the fifth phase of the Coupled Model Intercomparison project (CMIP5), i.e., NorESM1-M, and is therefore particularly suitable for multimillennial paleoclimate and carbon cycle simulations or large ensemble simulations. The speed-up is primarily a result of using a prescribed atmosphere aerosol chemistry and a tripolar ocean–sea ice horizontal grid configuration that allows an increase of the ocean–sea ice component time steps. Ocean biogeochemistry can be activated for fully coupled and semi-coupled carbon cycle applications. This paper describes the model and evaluates its performance using observations and NorESM1-M as benchmarks. The evaluation emphasizes model stability, important large-scale features in the ocean and sea ice components, internal variability in the coupled system, and climate sensitivity. Simulation results from NorESM1-F in general agree well with observational estimates and show evident improvements over NorESM1-M, for example, in the strength of the meridional overturning circulation and sea ice simulation, both important metrics in simulating past and future climates. Whereas NorESM1-M showed a slight global cool bias in the upper oceans, NorESM1-F exhibits a global warm bias. In general, however, NorESM1-F has more similarities than dissimilarities compared to NorESM1-M, and some biases and deficiencies known in NorESM1-M remain.

scholarly journals An observation-based scaling model for climate sensitivity estimates and global projections to 2100

2020 ◽  
Author(s):  
Raphaël Hébert ◽  
Shaun Lovejoy ◽  
Bruno Tremblay
Keyword(s):  
Climate Sensitivity ◽  
Coupled Model ◽  
Internal Variability ◽  
Forced Response ◽  
Scaling Exponent ◽  
Climate Response ◽  
Transient Climate Response ◽  
Rcp 8.5 ◽  
Historical Temperature ◽  
Inner Scale

AbstractWe directly exploit the stochasticity of the internal variability, and the linearity of the forced response to make global temperature projections based on historical data and a Green’s function, or Climate Response Function (CRF). To make the problem tractable, we take advantage of the temporal scaling symmetry to define a scaling CRF characterized by the scaling exponent H, which controls the long-range memory of the climate, i.e. how fast the system tends toward a steady-state, and an inner scale $$\tau \approx 2$$ τ ≈ 2   years below which the higher-frequency response is smoothed out. An aerosol scaling factor and a non-linear volcanic damping exponent were introduced to account for the large uncertainty in these forcings. We estimate the model and forcing parameters by Bayesian inference which allows us to analytically calculate the transient climate response and the equilibrium climate sensitivity as: $$1.7^{+0.3} _{-0.2}$$ 1 . 7 - 0.2 + 0.3   K and $$2.4^{+1.3} _{-0.6}$$ 2 . 4 - 0.6 + 1.3   K respectively (likely range). Projections to 2100 according to the RCP 2.6, 4.5 and 8.5 scenarios yield warmings with respect to 1880–1910 of: $$1.5^{+0.4}_{-0.2}K$$ 1 . 5 - 0.2 + 0.4 K , $$2.3^{+0.7}_{-0.5}$$ 2 . 3 - 0.5 + 0.7   K and $$4.2^{+1.3}_{-0.9}$$ 4 . 2 - 0.9 + 1.3   K. These projection estimates are lower than the ones based on a Coupled Model Intercomparison Project phase 5 multi-model ensemble; more importantly, their uncertainties are smaller and only depend on historical temperature and forcing series. The key uncertainty is due to aerosol forcings; we find a modern (2005) forcing value of $$[-1.0, -0.3]\, \,\,\mathrm{Wm} ^{-2}$$ [ - 1.0 , - 0.3 ] Wm - 2 (90 % confidence interval) with median at $$-0.7 \,\,\mathrm{Wm} ^{-2}$$ - 0.7 Wm - 2 . Projecting to 2100, we find that to keep the warming below 1.5 K, future emissions must undergo cuts similar to RCP 2.6 for which the probability to remain under 1.5 K is 48 %. RCP 4.5 and RCP 8.5-like futures overshoot with very high probability.

Anthropogenic and Natural Contributions to the Lengthening of the Southern Hemisphere Summer Season

2020 ◽  
Vol 33 (24) ◽  
pp. 10539-10553
Author(s):  
Evan Weller ◽  
Bo-Joung Park ◽  
Seung-Ki Min
Keyword(s):  
Greenhouse Gas ◽  
Southern Hemisphere ◽  
Dominant Role ◽  
Coupled Model ◽  
Internal Variability ◽  
Southern Annular Mode ◽  
Summer Season ◽  
External Forcings ◽  
Regional Trends

AbstractThis study provides the first quantitative assessment of observed long-term changes in summer-season timing and length in the Southern Hemisphere (SH) and its subregions over the past 60 years, enabling a global completeness by complementing such characteristics previously reported for the Northern Hemisphere (NH). Using an objective algorithm that is based on temperature indices, relative measures of summer onset, withdrawal, and duration are determined at each land location over the period 1953–2012. Significant widespread summer-season lengthening, due to earlier onset and delayed withdrawal, has occurred across the SH, a longer period for extreme heat-wave events and wildfires to potentially occur. The asymmetric magnitude (onset vs withdrawal) in summer-season lengthening is slightly less over the SH than over the NH. Contributions of anthropogenic and natural factors to the observed trends in summer-season characteristics were investigated using phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel simulations integrated with observed external forcings [anthropogenic plus natural (ALL)], greenhouse gas forcing only (GHG), and natural forcing only [solar and volcanic activities (NAT)]. Overall, consistent with the NH, increased greenhouse gases were the main cause of observed changes in the SH, with negligible contribution from other external forcings. ALL and GHG simulations also reproduced a slight tendency for earlier summer onset to contribute more to summer lengthening. Proportions of observed regional trends in summer-season indices attributable to trends in long-term internal variability in the SH, namely, the interdecadal Pacific oscillation (IPO) and southern annular mode (SAM), suggests such variability can only explain up to ~12%, supporting the dominant role of greenhouse gas forcing.

scholarly journals Physical processes of cooling and mega-drought during the 4.2 ka BP event: results from TraCE-21ka simulations

2019 ◽  
Vol 15 (1) ◽  
pp. 265-277 ◽  
Author(s):  
Mi Yan ◽  
Jian Liu
Keyword(s):  
North Atlantic ◽  
Wave Train ◽  
Internal Variability ◽  
Meridional Overturning Circulation ◽  
Physical Processes ◽  
Dynamic Background ◽  
External Forcings ◽  
Climate Simulations ◽  
Meridional Overturning

Abstract. It is widely believed that multi-decadal to centennial cooling and drought occurred from 4500 to 3900 BP, known as the 4.2 ka BP event that triggered the collapse of several cultures. However, whether this event was a global event or a regional event and what caused this event remains unclear. In this study, we investigated the spatiotemporal characteristics, the possible causes and the related physical processes of the event using a set of long-term climate simulations, including one all-forcing experiment and four single-forcing experiments. The results derived from the all-forcing experiment show that this event occurs over most parts of the Northern Hemisphere (NH), indicating that this event could have been a hemispheric event. The cooler NH and warmer Southern Hemisphere (SH) illustrate that this event could be related to the slowdown of the Atlantic Meridional Overturning Circulation (AMOC). The comparison between the all-forcing experiment and the single-forcing experiments indicates that this event might have been caused by internal variability, while external forcings such as orbital and greenhouse gases might have modulation effects. A positive North Atlantic Oscillation (NAO)-like pattern in the atmosphere (low troposphere) triggered a negative Atlantic Multi-decadal Oscillation (AMO)-like pattern in the ocean, which then triggered a circum-global teleconnection (CGT)-like wave train pattern in the atmosphere (high troposphere). The positive NAO-like pattern and the CGT-like pattern are the direct physical processes that led to the NH cooling and mega-drought. The AMO-like pattern played a “bridge” role in maintaining this barotropic structure in the atmosphere at a multi-decadal–centennial timescale. Our work provides a global image and dynamic background to help better understand the 4.2 ka BP event.

Which climate models capture the variability and forced response in observed temperatures: a large ensemble comparison

2020 ◽  
Author(s):  
Nicola Maher ◽  
Laura Suarez-Gutierrez ◽  
Sebastian Milinski
Keyword(s):  
Climate Models ◽  
Internal Variability ◽  
Forced Response ◽  
Tropical Eastern Pacific ◽  
The North ◽  
Surface Temperatures ◽  
External Forcings ◽  
Coupled Climate Models ◽  
Large Ensembles ◽  
Adequate Representation

<p>We evaluate how large ensembles of ten coupled climate models represent the observed internal variability and response to external forcings in historical surface temperatures based on a novel methodological framework. This framework allows us to directly attribute whether discrepancies between models and observations arise due to biases in the simulated internal variability or rather in the forced response, without relying on assumptions to separate both signals in the observations. The largest discrepancies occur due to overestimated forced warming in some models during recent decades. The areas where most models, a maximum of nine, adequately simulate observed temperatures are the North Atlantic, Tropical Eastern Pacific, and the Northern Hemisphere land areas. In contrast, none of the models considered offers an adequate representation over the Southern Ocean. Our evaluation shows that CESM-LE, GFDL-ESM2M, and MPI-GE perform best at representing the internal variability and forced response in observed surface temperatures both globally and regionally. </p>

scholarly journals Detection of Human Influence on Precipitation Extremes in Asia

2020 ◽  
Vol 33 (12) ◽  
pp. 5293-5304 ◽  
Author(s):  
Siyan Dong ◽  
Ying Sun ◽  
Chao Li
Keyword(s):  
High Latitude ◽  
Extreme Precipitation ◽  
Signal Analysis ◽  
Coupled Model ◽  
Internal Variability ◽  
Precipitation Extremes ◽  
Anthropogenic Influence ◽  
Significance Level ◽  
External Forcings ◽  
Extreme Indices

AbstractThis paper examines the possible influence of external forcings on observed changes in precipitation extremes in the mid-to-high latitudes of Asia during 1958–2012 and attempts to identify particular extreme precipitation indices on which there are better chances to detect the influence of external forcings. We compare a recently compiled dataset of observed extreme indices with those from phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations using an optimal fingerprinting method. We consider six indices that characterize different aspects of extreme precipitation, including annual maximum amount of precipitation falling in 1 day (Rx1day) or 5 days (Rx5day), the total amount of precipitation from the top 5% or top 1% daily amount on wet days, and the fraction of the annual total precipitation from these events. For single-signal analysis, the fingerprints of external forcings including anthropogenic agents are robustly detected in most studied extreme indices over all Asia and for midlatitude Asia but not for high-latitude Asia. For two-signal analysis, anthropogenic influence is detectable in these indices over Asia at 5% or slightly less than 5% significance level, whereas natural influence is not detectable. In high-latitude Asia, anthropogenic influence is detected only in a fractional index, representing a stark contrast to the midlatitude and full Asia results. We find relatively smaller internal variability and thus higher signal-to-noise ratio in the fractional indices when compared with the other ones. Our results point to the need for studying precipitation extreme indices that are less affected by internal variability while still representing the relevant nature of precipitation extremes to improve the possibility of detecting a forced signal if one is present in the data.

Exploiting large ensembles for a better yet simpler climate model evaluation

2021 ◽  
Author(s):  
Laura Suarez-Gutierrez ◽  
Sebastian Milinski ◽  
Nicola Maher
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Internal Variability ◽  
Forced Response ◽  
Evaluation Framework ◽  
Surface Areas ◽  
Global Mean Temperature ◽  
External Forcings ◽  
Large Ensembles

<p>We use a methodological framework exploiting the power of large ensembles to evaluate how well ten coupled climate models represent the internal variability and response to external forcings in observed historical surface temperatures. This evaluation framework allows us to directly attribute discrepancies between models and observations to biases in the simulated internal variability or forced response, without relying on assumptions to separate these signals in observations. The largest discrepancies result from the overestimated forced warming in some models during recent decades. In contrast, models do not systematically over- or underestimate internal variability in global mean temperature. On regional scales, all models misrepresent surface temperature variability over the Southern Ocean, while overestimating variability over land-surface areas, such as the Amazon and South Asia, and high-latitude oceans. Our evaluation shows that MPI-GE, followed by GFDL-ESM2M and CESM-LE offer the best global and regional representation of both the internal variability and forced response in observed historical temperatures.</p>

Sign in / Sign up

Export Citation Format

Share Document