Displays

The Equilibrium Climate Sensitivity (ECS) remains not very well constrained, either by climate models, observational, historical or palaeoclimate data. In particular, large values of warming as a consequence of atmospheric greenhouse gas increase cannot be excluded. Interestingly, some of the most recent state-of-the-art climate models (CMIP6) suggest much more warming than previous generations of climate models. Next to the classical (measurement) uncertainty, the spread in ECS values is due to dynamical aspects: 

1. The climate system has strong internal variability on many timescales such that the 'equilibrium' will only be relative to fixing slow processes. This implies the assumption that time scale separation exists and ECS values from palaeoclimate time series can be compared to short model simulations. Palaeoclimate records often determine the Earth System Sensitivity, which includes the integrated effect of slow processes and boundary conditions (e.g. geography, vegetation and land ice).
2. The background state dependence of fast feedback processes: Information from the late Pleistocene ice age cycles indicates that ECS varies considerably between regime because of fast feedback processes changing their relative strength over one cycle.
3. Tipping elements in the climate system: Extreme values of palaeo-derived ECS suggest that the climate response is in a region where the assumption of linear response to perturbations breaks down. 

Here we show for climate system models with more than one regime and occasional switches between these regimes, we can empirically determine probability of change in regime and confirm that extremes of climate sensitivity are associated with very high probabilities of tipping.

New to CMIP6 is the Tier 1 lig127k experiment, designed to address the climate responses to stronger orbital forcing than the midHolocene experiment, using the same state-of-the-art models and following a common experimental protocol. We present a multi-model ensemble of 17 climate models, all of which (except for two) have also completed the CMIP6 DECK experiments, looking at the lig127k Arctic’s responses across models and the relationships with each model’s Equilibrium Climate Sensitivity (ECS), preindustrial sea ice thickness and 127ka temperature anomalies.

Boreal insolation anomalies at 127 ka enhance the seasonal cycle of Arctic sea ice, though with notable differences among the models. The consensus from the lig127k sea ice distributions is a reduced minimum (August-September) summer sea ice extent in the Arctic as compared to the piControl simulations. Sea ice remains above 15% concentrations over the central Arctic Ocean in all but one of the lig127k simulations. More than half of the models simulate a retreat of the Arctic minimum ice edge similar to the average of the last 2 decades. The lig127k minimum Arctic sea ice area anomalies show a strong negative correlation with the Arctic (60-90°N) annual surface temperature anomalies but only a weak correlation with the corresponding June-July-August (JJA) temperature anomalies. Memory in the ocean and cryosphere provide feedbacks to maintain larger positive temperature anomalies, December-January-February (DJF) and annually, in the Arctic than in JJA. The models contributing to the lig127k ensemble have an ECS varying from 2.1 to 5.3°C. There is a notable relationship between the ECS and simulation of lig127k minimum Arctic sea ice area.  With very limited Arctic sea ice proxies for 127 ka, and with evolving interpretation of the relationships of these proxies with sea ice coverage, it is still difficult to rule out the high or low values of ECS from the proxy data.

In recent decades the paleo-modelling community has sought to identify past warm climates that could provide analogues for greenhouse induced warming. In spite of some similarities in temperature distributions (e.g. Pliocene, Eocene, Cretaceous and summertime Northern Hemisphere mid-Holocene), however, it is unlikely that any past epoch can provide detailed insight into future warming, especially in terms of changes in the hydrological cycle. Reviewing recent work, we show that changes in the atmospheric circulation can dramatically alter the relationship between temperature and precipitation, weakening the possibility for useful climate analogs as envisioned in the literature. We present results of moisture budget decomposition from mid-Holocene and Representative Pathways Scenario RCP8.5, two climates in which monsoons are stronger and wider than the pre-Industrial era. We find that Northern Hemisphere monsoons are much stronger and wider during the Holocene than what projected for the end of the 21st century. This is because the thermodynamic (i.e. moisture changes) and dynamic responses (i.e. mean-flow changes) reinforce each other in the mid-Holocene while they partially cancel out in the future climate. Therefore, the Holocene does not represent an analogue of the future given the opposite dynamical responses in the two climates. Consistent with other studies, our work highlights that changes in atmospheric circulation are the major source of uncertainty for future projection of hydrological cycle, especially at regional scales.

The Earth’s climate is characterized by many modes of variability. On millennial timescales, decaying Northern Hemisphere ice sheets during the last deglaciation affect the high latitude hydrological balance in the North Atlantic and therefore the ocean circulation after the Last Glacial Maximum. Global sea-level reconstructions indicate marked abrupt changes within several hundred years. Using a multi-scale climate model with a high resolution near the coast, we find a strong sensitivity of the ocean circulation depending on where the deglacial meltwater is injected. Meltwater injections via the Mississippi and near Labrador hardly affect the AMOC. The reduced sensitivity of the overturning circulation against freshwater perturbations following the Mississippi route provides a consistent representation of the deglacial climate evolution. A subpolar North Atlantic Ocean freshening, mimicking a transport of water by icebergs, yields, on the other hand, a quasi-shutdown. We can conclude that millennial climate variability depends on the spatio-temporal structure and their representation in models.

Millennial DO-like variability is seen in a handful of model simulations, including even some pre-industrial simulations. As a mechanism, the subsurface is warmed by the downward mixing of the warmer overlying water during an AMOC weak state, until the surface became denser than at mid-depth and deep ventilation is initiated. In recent model developments, the large oscillations in the Labrador Sea mixing were reduced. However, it might be that the centennial-to-millennial oscillations are required to explain climate variability as expressed e.g. by the Little Ice age and the Medieval Warm Event during the last 1000 years. It could be that a de-tuning of the models is necessary in order to exhibit irregular oscillations on centennial-to-millennial time-scales. Although a systematical analysis of the mechanisms leading to centennial-to-millennial variability remains open, numerical experiments suggest that at least in the Labrador Sea and other sensitive areas the high resolution can play an important role in realistically simulating the variability in the mixed layer depth affecting AMOC. One can question regularities found in DO-events occurrence and statistically analyzed the distribution of inter-event waiting times. To estimate the statistical significance of detected event patterns, we construct a simple stochastic process in which events are triggered each time a threshold criterion is fulfilled. For a given time interval each event occurs therefore stochastically independent of another meaning that the probability of one abrupt warming does not affect the probability distribution of any other warming events in that interval. Additionally, novel periodicities of ∼900 and ∼1150 yrs in the NGRIP record are reported besides the prominent 1500 yrs cycle but demonstrate that although a high periodicity reflected in a high Rayleigh R can be found in the data it remains indistinguishable from a simple stationary random Poisson process. These are quite interesting findings as ∼1500 and ∼900 yrs periods are visible throughout the Holocene. The understanding of such low-frequency variability is crucial to allow a separation of anthropogenic signals from natural variability. 

Global warming projections for a given anthropogenic greenhouse gas concentration scenario still exhibit a substantial spread.  In order to constrain this spread and to provide robust warming projections, our understanding of Earth's climate sensitivity needs to be further improved. Here, we employ a global network of 64 paleo-proxies of SST to reconstruct global-mean SST variations during the Last Glacial Cycle. This temperature reconstruction is then used as a target function for 25 transient model simulations conducted for the same period with 25 different climate sensitivities. Our combined proxy/model approach allows us to determine an optimal range of model climate sensitivities corresponding to a minimum of the weighted mean squared error calculated for reconstructed and simulated global-mean SSTs. Based on our best estimate, Earth's averaged Late Pleistocene equilibrium climate sensitivity is in the order of ~4.2 K per CO₂ doubling with an associated transient climate response of ~2.4 K/2xCO₂. The latter value translates into a global-mean surface warming of about 5.0 K by the year 2100 (relative to pre-industrial levels) based on the Representative Concentration Pathway 8.5. This warming estimate is in excellent agreement with the ensemble-mean projection of climate simulations conducted as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Error bars resulting from uncertainties in aerosol and ice-sheet forcing as well as in temperature reconstruction clearly document the current limitations for paleo-based constrains of both climate sensitivity and future greenhouse warming and demonstrate the need for more robust forcing and temperature reconstructions that can be utilized to narrow down the spread in global warming projections.

In this study we introduce a Bayesian framework, which is flexible and explicit about the prior assumptions, for using model ensembles and observations together to constrain future climate change. The emergent constraint approach has seen broad application in recent years, including studies constraining the equilibrium climate sensitivity (ECS) using the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period (mPWP). Most of these studies were based on Ordinary Least Squares (OLS) fits between a variable of the climate state, such as tropical temperature, and climate sensitivity. Using our Bayesian method, and considering the LGM and mPWP separately, we obtain values of ECS of 2.7 K (1.1 - 4.8, 5 - 95 percentiles) using the PMIP2, PMIP3 and PMIP4 data sets for the LGM, and 2.4 K (0.4 - 5.0) with the PlioMIP1 and PlioMIP2 data sets for the mPWP. Restricting the ensembles to include only the most recent version of each model, we obtain 2.7 K (1.1 - 4.3) using the LGM and  2.4 K (0.4 - 5.1) using the mPWP. An advantage of the Bayesian framework is that it is possible to combine the two periods assuming they are independent, whereby we obtain a slightly tighter constraint of 2.6 K (1.1 - 3.9). We have explored the sensitivity to our assumptions in the method, including considering structural uncertainty, and in the choice of models, and this leads to 95% probability of climate sensitivity mostly below 5 and never exceeding 6 K. The approach is compared with other approaches based on OLS, a Kalman filter method and an alternative Bayesian method. An interesting implication of this work is that OLS-based emergent constraints on ECS generate tighter uncertainty estimates, in particular at the lower end, suggesting a higher bound by construction in case of weaker correlation. Although some fundamental challenges related to the use of emergent constraints remain, this paper provides a step towards a better foundation of their potential use in future probabilistic estimation of climate sensitivity.

The transient climate response (TCR), transient warming for a doubling of CO2, contributes the biggest uncertainty to estimates of the carbon budgets consistent with the Paris targets. In the IPCC 5th Assessment Report (AR5), the stated ‘likely’ range of TCR was given as 1.0 to 2.5K, with a central estimate which was broadly consistent with the ensemble mean of the CMIP5 Earth System Models (ESMs) available at the time (1.8 +/- 0.4 K) . Many of the latest CMIP6 ESMs have larger climate sensitivities, with 6 of 23 models having TCR values above 2.5 K, and an ensemble mean TCR of 2.1 +/- 0.4 K. On the face of it, these latest ESM results suggest that the IPCC likely range of TCRE may need revising upwards, which would cast further doubt on the feasibility of the Paris targets.

We analyse the CMIP6 models through an emergent constraint approach which relates the value of TCR to the global warming from 1970 onwards. We confirm a consistent emergent constraint on TCR when we apply the same method to CMIP5 model. Our emergent constraint on TCR benefits from both the large range of TCR values across the CMIP6 models, and also from the extension of the historical simulations into a period when the uncertain changes in aerosol forcing have had a far less significant impact on the trend in global warming.

We show that rather than increasing the uncertainty in climate sensitivity, the CMIP6 models help to further constrain the likely range of TCR to 1.5-2.2 K. In CMIP6, diagnosed emissions at carbon doubling was found to be independent of TCR, so that a constraint on TCR directly leads to a constrained estimate of TCRE, with a likely range of 1.3 – 2.0 K per EgC. 

Surface Melt (SM) is one of the factors that contribute to sea level rise; surface meltwater draining through the ice and beneath Antarctic glaciers may cause acceleration in their flow towards the sea. Changes in the frequency of relatively warm days (including heatwaves) can substantially alter the SM variability, thus leading to extreme melting events. By using simulations from 13 Global Climate Models (GCMs) and according to a moderate representative concentration pathways (RCP4.5), here we show that the frequency of extreme SM events (SM90; according to the 90th percentile over the reference period 1961-1990) may significantly increase in coastal areas of West Antarctica; in particular in the Antarctic Peninsula. By the end of the century SM90 estimates are expected to increase from currently 0.10 kg/m2/day to about 0.45 kg/m2/day in the Antarctic Peninsula. Increments in SM90 estimates are not just driven by changes in the average SM, but also by the variability in SM. The latter is expected to increase by around 50% in the Antarctic Peninsula.

It is virtually certain that the mean surface temperature of the Earth will continue to increase under realistic emission scenarios. Yet comparatively little is known about future changes in climate variability. We explore changes in climate variability over the large range of climates simulated in the framework the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5/6) and the Paleoclimate Modeling Intercomparison Project Phases 3 and 4 (PMIP3/4). 
This consists of time slice simulations for the Pliocene, Last Interglacial, Last Glacial Maximum, the Mid Holocene and idealized warming experiments (1% CO₂ and abrupt 4xCO₂), and encompasses climates with a range of 12°C of global mean temperature change. We examine climate variability from different perspectives: from local interannual change, to coherent climate modes and through compositing extremes. The change in the interannual variability of precipitation is strongly dependent upon the local change in the total amount of precipitation. Meanwhile only over tropical land is the change in the interannual temperature variability positively correlated to temperature change, and then weakly. In general, temperature variability is inversely related to mean temperature change - with analysis of power spectra demonstrating that this holds from intra-seasonal to multi-decadal timescales. We systematically investigate changes in the standard deviation of modes of climate variability. Overall, no generalisable pattern emerges. Several modes do show, sometimes weak, increasing variability with global mean temperature change (most notably the Atlantic Zonal Mode), but also the El Niño/Southern Oscillation indices (NINO3.4 and NINO4). The annular modes in the Northern (Southern) hemisphere show only weakly increasing (decreasing) relationships.
By compositing extreme precipitation events across the ensemble, we demonstrate that the atmospheric drivers dominating rainfall variability in Mediterranean climates persist throughout palaeoclimate and future simulations. The robust nature of the response of climate variability in model simulations, between both cold and warm climates and across multiple timescales, suggests that observations and proxy reconstructions could provide a meaningful constraint on climate variability in future projections.

Multidecadal to millenial timescale climate variability has been investigated over the ocean

using extensive proxy data and it was found to yield coherent interproxy estimates of global and regional sea-surface temperature (SST) climate variability (Laepple and Huybers, 2014). Global Climate Model (GCM) simulations on the other hand, were found to exhibit an increasingly large deficit of regional SST climate variability for increasingly longer timescales.

Further investigation is needed to better quantify terrestrial climate variability for long

timescales and validate climate models.

Vegetation related proxies such as tree rings and pollen records are the most widespread

types of archives available to investigate terrestrial climate variability. Tree ring records are

particularly useful for short time scales estimates due to their annual resolution, while pollen-based reconstructions are necessary to cover the longer timescales. In the present work, we use a large database of 1873 pollen records covering the northern hemisphere in order to quantify Holocene vegetation and climate variability for the first time at centennial to multi-millenial timescales.

To ensure the robustness of our results, we are particularly interested in the spatio-temporal representativity of the archived signal in pollen records after taking into account the effective spatial scale, the intermittent and irregular sampling, the age-uncertainty and the sediment mixing effect. A careful treatment of the proxy formation allows us to investigate the spatial correlation structure of the pollen-based climate reconstructions as a function of timescales. The pollen data results are then contrasted with the analysis replicated using transient Holocene simulations produced with state-of-the-art climate models as well as stochastic climate model simulations.Our results indicate a substantial gap in terrestrial climate variability between the climate model simulations and the pollen reconstructions at centennial to multi-millenial timescales, mirroring the variability gap found in the marine domain. Finally, we investigate how future climate model projections with greater internal variability would be affected, and how this increases the uncertainty of regional land temperature projections.

Model simulations of the last glacial maximum (LGM) and RCP8.5 projections suggest that factors responsible for past and future changes in the Mediterranean region are different.  The wet LGM conditions were determined mainly by low evaporation, with some increase of precipitation in the western areas, while dry rcp8.5 conditions will be driven by a reduction of precipitation over the whole region. These changes were caused by atmospheric dynamics (changes of mean atmospheric circulation ) in LGM and it will be caused by the atmospheric thermodynamics (reduction of mean moisture content ) in the future rcp8.5. In both cases, the Mediterranean region appears to be more sensitive to climate change than the rest of areas within the same latitudinal range, particularly considering the hydrological cycle, whose characteristics in winter exhibit large changes between these two different climates. These conclusions emerge from the substantial consensus among six PMIP3 and CMIP5 models, simulating LGM, pre-Industrial and rcp8.5 climate conditions.

The Atlantic Meridional Overturning Circulation (AMOC) is essential to maintain the temperate climates of Europe and North America. It redistributes heat from the tropics, and stores carbon in the deep ocean. Yet, its variability and evolution are largely unknown due to the lack of long-term direct circulation measurements. Previous studies suggest a connection between the variability of the AMOC strength and a temperature dipole in the North Atlantic. These results suggest a substantial decline in the strength of the overturning at the onset of the industrial era. 

Here we compare temperature reconstructions from four sediment cores in the North Atlantic with model simulations of the Community Earth System Model (CESM1) as well as the Hadley Centre Coupled Model (HadCM3) over the Common Era. By examining the correlation between the surface temperatures in the North Atlantic and the strength of the overturning we test the robustness of previously used temperature fingerprints. Analysing variability in the surface and subsurface temperatures as well as the overturning strength in models we assess possible drivers of variability in ocean circulation. We compare the persistence times and the time scale dependent variability of the AMOC, the surface and ocean temperatures in the model with those in the temperature reconstructions. The sub-surface reconstructions match with the 200m ocean temperatures in persistence times but not with the AMOC in the models. The surface temperatures in the models show persistence times similar to those obtained for the AMOC. However, time scale dependent variabilities in the surface temperatures do not match those found the AMOC. Therefore, temperature fingerprints might not be a reliable basis to reconstruct the ocean overturning strength.

Due to the systematic comparison of two models on different time scales and an assessment of surface to sub-surface temperatures this study could provide new insights into the variability of Atlantic overturning on decadal time scales and beyond.

Climate field reconstruction (CFR) techniques have become common tools for studying terrestrial climate variability across various time and space scales^1,2. Here, we present the framework of ensemble-based CFR of sea surface temperature in the North Atlantic Ocean, using a Bayesian hierarchical model⁵ and the proxy surrogate reconstruction method⁶. Methodological development is necessary in order to take properly into account the age-depth uncertainties that is specific to proxies originating from marine sediment cores, and the generally low temporal resolution of the data. The new reconstructions will provide evidence on linkages and mechanisms between the marine realm and European climate including extremes covering the past 2000 years. The spatiotemporal covariance-structures of the input and output data are of special relevance, and the benchmarking datasets can be used for various impact studies and for paleomodel/data comparison and process understanding coupling the North Atlantic with Eurasia.

References:

¹Luterbacher, J. & Zorita, E. in “The Palgrave Handbook of Climate History”, (2018), Palgrave Macmillan, Springer Nature Limited,London, 131-140, ISBN 978-1-137-43020-5 (eBook), doi: 10.1057/978-1-137-43020-5

²Neukom, R. et al. (2019), Nature, 571, 550–554, doi:10.1038/s41586-019-1401-2

³ Tingley, M. P. and P. Huybers (2010a), J. Clim., 23, 2759–2781, doi: 10.1175/2009JCLI3015.1

⁴ Graham, N.E. et al. (2007), Clim. Change, 83, 241-285, doi: 10.1007/s10584-007-9239-2

Climate variability influences the probability of extreme events and is therefore of great importance for risk management. Nevertheless, changes in climate variability over time are far less studied than changes in the mean state of the climate system. Proxy records can be used to estimate the dependency of climate variability on the state and timescale, but their climate signal is perturbed by non-climatic processes and dating uncertainties. Analyzing ice cores and marine sediments, it was shown that temperature variability during the Last Glacial Maximum was larger than in the Holocene and that the magnitude of variability change depends on latitude.

We estimate millennial and orbital scale variability in pollen records during the last Glacial. We draw on a global network of published pollen records, which are influenced by local temperature and moisture availability, and compare these estimates with temperature, precipitation, and vegetation variability in climate simulations of the last Glacial cycle. We discuss the regional consistency of timescale dependent estimates. Differences between Marine Isotope Stages 2, 3, and 4 are examined by comparing spatial patterns during those three periods. Then, we use spectral methods to study the scaling behavior of the pollen records. This provides additional information on the continuum of variability from centennial to orbital scales. Finally, we quantify the co-occurrence of millennial and orbital scale fluctuations across different pollen records with paleoclimate network techniques.

Our work extends previous estimates to the terrestrial realm and to longer timescales. The results provide new insight on the climate variability differences between glacial and interglacial states, and on the mismatch between climate simulations and proxy data.

More than 90% territory of Russia influenced by modern and relict cryolithogenesis (Velichko, 1996). Many relict periglacial features bear witness of Late Pleistocene climate oscillation events and nowadays they are widespread in Mid-Latitude Western Europe including Russian territory. It is known, paleocryogenic factor influence on soil cover’s structure on the different geomorphological position. However interrelation problem between various type of relict cryogenic features (RCF) and modern geomorphological processes, especially erosion and sedimentation, and soil degradation stays unsearched.

The goal of research – to estimate, how RCF affects modern processes and soil cover structure within the agricultural areas (Yaroslavl and Kursk regions). The research also is concentrated on evaluation relationship between different types of the relic cryogenic features and intensity and spatial distribution of soil erosion and deposition processes on cultivated slopes.

Materials and Methods

This study is based on the analysis of aerial photographs (Sentinel, BingSat, Google, Yaundex), including DEMs and aero photos from air drone, and new field surveys. Also we used a group of methods to estimate erosion rates within the small catchments areas (soil profile morphology, analysis of Cesium-137 supply in soil, empirical-mathematical models USLE/ГГИ and WaTEM/SEDEM).  It is supposed to test modern methods (neuron net) for automatic decoding of paleocryogenic relief and creating an appropriate data set - contours or at least positions (centroids) of these forms.

Results

The relict permafrost-thermokarst relief prevails in the Yaroslavl Region; a polygonal relief with a block length of 40-50 m is visible almost everywhere. In new-ploughed fields  inside the polygons, a second generation of blocks with a side length of 10-20 m is visible.

To the south, on the territory covered with loess-loam soil stripes or trenches can be also detected. But on this southern territories relict cryogenic network are smaller, the relief of small knolls and depressions are widespread. They appeared due to ice-wedges melting.  An analysis of the structure of the erosion-channel network in the Kursk region showed that numerous small ravines and washed-out troughs, widespread on agricultural fields, largely inherit or developed due to the RCM forms.

Conclusions

The period of transition of active cryogenic forms to the relict state is associated with numerous processes of burial, redeposition and destruction of material and microrelief alignment.
RCF affects the structure and dynamics of modern erosion processes: shape and density of the erosion network; the direction, extent and complexity of the slope flows structure, the presence and alternation of redeposition and transit zones; sediment budget structure of elementary slope, gullys and small river catchment areas.

*This research is supported by the Russian Foundation for Basic Research (Project No. 18-05-01118a).

There is considerable uncertainty about how the rapid, recent rise in greenhouse gas concentrations driven by anthropogenic emissions will interact with on-going changes in orbital position to affect climate in the very long run – the next several thousand years. Here we study the evolution of climate over the next hundred thousand years using a statistical climate model estimated on the paleo record that represents physically consistent relations between orbital position and climate. This climate model is able to use orbital position alone to simulate the timing, magnitude, and saw-toothed pattern of ice volume, CO₂ concentrations, and other climate time series both in- and out-of-sample. The model is used to run experiments that simulate climate with- and without human intervention in the global carbon cycle. Without human intervention, the next glacial maximum is forecast to occur in about 20,000 years. This result is relatively unaffected by the current anthropogenic spike in CO₂ concentrations. Conversely, the glacial maximum can be avoided - and the current climate maintained - by geo-engineering carbon concentrations to stabilize at around 325 ppm. The emissions needed to sustain these concentrations can be generated from known resources of fossil fuels. This suggests that CO₂ is a cost effective control variable that - if managed carefully - can be used to sustain a hospitable climate in the short-run (by reducing emissions) and the long-run (by stabilizing concentrations).

Precipitation extremes and associated hazards pose a significant risk to society and the economy on a global scale. Effective mitigation strategies require accurate estimates of the intensity and frequency of those extremes. Traditional approaches for precipitation frequency analysis rely on long-record from in-situ observations, which however are not available on a global scale. Satellite and reanalysis-based products provide global precipitation estimates suitable for frequency analysis due to their extensive spatial coverage. However, errors in global precipitation products lead to significant bias in the quantification of extremes and potential changes. To examine this issue, five regions(Austria, north Italy, Florida, Texas, Arizona) that include a high-density gauge network (>3 gauges/satellite pixel) are selected as references to evaluate the uncertainty in retrieving extreme value statistics based on four global precipitation products (MSWEP, IMERG, GSMaP, CMORPH). The statistical properties of extremes are based on the application of the Metastatistical Extreme Value (MEV) framework. MEV has been validated in previous studies that have demonstrated that the method is able to provide robust estimates of high quantiles from short data records. In this work we evaluate the uncertainty on the estimation of extremes focusing primarily on the dependence to a) data characteristics and b) hydroclimatic region. Additionally, we evaluate the sub-grid variability of extreme precipitation and we demonstrate the impact of spatial scale mismatch (i.e. point vs satellite pixel) on the frequency analysis of extremes. This work provides a relatively comprehensive assessment of the use of MEV for estimating precipitation extremes from globally available datasets and an understanding of the variability of sub-daily precipitation extremes at different hydroclimatic regions of the world.

Among the sources of temporal variability in the climate system, an important role belongs to internal variability modes – phenomena with oscillatory behavior ranging from predominantly sub-annual (e.g. North Atlantic Oscillation) or inter-annual (e.g. Southern Oscillation) to decadal or multidecadal variations (e.g. Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation). These oscillations manifest themselves not only within their particular geographical areas of origin, but their effects are typically also transmitted through long-range teleconnections, affecting weather and climate patterns worldwide. Analysis of these relationships is often done assuming their linearity – but rarely is such assumption explicitly verified.

In this presentation, presence and magnitude of nonlinear components in long-range teleconnections associated with selected climate variability modes are studied through various time series analysis methods. Several nonlinearity-quantifying statistics, ranging from simple measures of asymmetry in the regression coefficients to outcomes of more formal surrogate data-based tests, are employed to investigate the teleconnection-related responses of local temperatures across the globe. It is shown that substantial variations exist in degree of manifested nonlinearity, subject to both the target location and type of the variability mode(s) considered. Potential of individual nonlinearity-sensitive techniques for more realistic capture of the teleconnection-related response patterns is also discussed, with an ultimate goal of construction of a more accurate model of variability transfer in the climate system.

Solar irradiance is a crucial input to climate models, but its measurements are only available since 1978. The variability of solar irradiance on climate-relevant time-scales is caused by the competition between bright and dark features formed by the magnetic fields emerging on the solar surface. Thus, models have been developed that reconstruct past irradiance variability from proxies of the solar magnetic activity. The longest direct proxy is the sunspot number. The common problem of such reconstructions is, however, that while sunspots adequately describe the evolution of the active regions (ARs) (large bipolar regions hosting sunspots), the evolution of their smaller counterparts, the ephemeral regions (ERs), is not directly featured by sunspots. At the same time, these small regions are much more numerous and are believed to be the main source of the long-term irradiance changes, which are of special interest to climate models. We develop an improved description of the ephemeral region emergence taking different solar observational constraints into account. The model builds on the SATIRE-T model, in which the emergence of ARs is described by the sunspot number and the emergence of the ERs is linearly linked to that of ARs. The latter, however, implies that whenever the sunspot number drops to zero, no magnetic field emerges in the model. In the new model, the emergence of the ERs is no longer linked to sunspots linearly. Instead, ARs and ERs are considered to be parts of a single power-law size distribution of the emerging magnetic regions. This ensures that even in the absence of ARs (e.g., during the grand minima of solar activity), the emergence rate of ERs remains non-zero. In particular, the solar open magnetic flux reconstructed using this approach does not drop to zero during the Maunder minimum, in agreement with independent reconstructions from the cosmogenic isotope data. Such an improved description of the ERs will allow a better constraint on the maximum solar irradiance drop during grand minima events. This, in turn, will allow a better constraint on the potential solar forcing in the future.

The conventional energy balance equation (EBE) is a first order linear differential equation driven by solar, volcanic and anthropogenic forcings.  The differential term accounts for energy storage usually modelled as one or two “boxes”.  Each box obeys Newton’s law of cooling, so that when perturbed, the Earth’s temperature relaxes exponentially to a thermodynamic equilibrium.

However, the spatial scaling obeyed by the atmosphere and its numerical models implies that the energy storage process is a scaling, power law process, a consequence largely of turbulent, hierarchically organized oceans currents but also hierarchies of land ice, soil moisture and other processes whose rates depend on size.

Scaling storage leads to power law relaxation and can be modelled via a seemingly trivial change - from integer to fractional order derivatives - the Fractional EBE (FEBE): with temperature derivatives order 0 < H  < 1 rather than the EBE value H = 1.  Mathematically the FEBE is a past value problem, not an initial value problem.    Recent support for the FEBE comes from [Lovejoy, 2019a] who shows that the special H = 1/2 case (close to observations), the “Half-order EBE” (HEBE), can be analytically obtained from classical Budyko-Sellers energy balance models by improving the boundary conditions.

The FEBE simultaneously models the deterministic forced response to external (e.g. anthropogenic) forcing as well as the stochastic response to internal forcing (variability) [Lovejoy, 2019b].  We directly exploit both aspects to make projections based on historical data estimating the parameters using Bayesian inference.  Using global instrumental temperature series, alongside CMIP5 and CMIP6 standard forcings, the basic FEBE parameters are H ≈ 0.4 with a relaxation time ≈ 4 years.  

This observation-based model also produces projections for the coming century with forcings prescribed by the CMIP5 Representative Concentration Pathways scenarios and the CMIP6 Shared Socioeconomic Pathways.

We compare both generations of General Circulation Models (GCMs) outputs from CMIP5/6 alongside with the projections produced by the FEBE model which are entirely independent from GCMs, contributing to our understanding of forced climate variability in the past, present and future.  When comparing to CMIP5 projections, we find that the mean projections are about 10- 15% lower while the uncertainties are roughly half as large.  Our global temperature projections are therefore within the  CMIP5 90% confidence limits and thus give them strong, independent support.

References

Lovejoy, S., The half-order energy balance equation, J. Geophys. Res. (Atmos.), (submitted, Nov. 2019), 2019a.

Lovejoy, S., Fractional Relaxation noises, motions and the stochastic fractional relxation equation Nonlinear Proc. in Geophys. Disc., https://doi.org/10.5194/npg-2019-39, 2019b.

Using historical observations to constrain climate projections is an old idea. A variety of approaches, time periods and scales have been used to this purpose. Simultaneously, detection and attribution (D&A) methods have been developed to assess the contribution of subsets of forcings to historical changes, and have also been used to constrain projections. Here, we describe a unified statistical method to constrain the entire forced response pathway of global mean temperature using an adaptation of Kriging. We start by introducing this new statistical approach. Then, we derive consistent observationally-constrained estimates of attributable warming to date for various forcings, attributable warming rate, the response to various scenarios, Transient Climate Response (TCR), and Equilibrium Climate Sensitivity (ECS). Using revised observations of near-surface atmospheric temperature, we estimate a total forced warming of 1.19+/-0.15°C in 2019, with respect to the 1850-1900 baseline. Based on the newly available CMIP6 ensemble, we find that historical observations narrow uncertainty on past and future warming by about 50%, while evidence suggests that the proposed technique is not over-confident. Remarkably, both sides of uncertainty ranges are affected, leading to a 5–95% range for TCR of 1.44–2.35°C. We also compare and discuss the differences between the CMIP5 and CMIP6 ensembles. The proposed method is easily transposable, thus opening the possibility to monitor climate change and narrow uncertainty at the regional scale and/or for different climate variables.

This research compared a set of past projections made by the Intergovernmental Panel on Climate Change (IPCC) to observations originating from both gauged stations and satellite products. Three IPCC assessment reports were taken into account (First, Second and Third assessment reports- FAR, SAR and TAR) and for each, two scenarios from various models were chosen. The period 1998-2005 was considered. A focus was given to West Africa, which was divided in 3 subregions following the latitudes and two main variables for the region were analyzed: precipitation and temperature. The analyses were conducted on mean annual values and monthly annual cycles both at subregional and regional levels. They revealed that the differences are greater on lower latitudes and depend a lot on the scenarios. The Business-as-Usual scenario which assumes that few or no steps are taken to limit greenhouse gas emissions seems to be the one that is the closest to the observations. The relative importance and potential implications of the differences between projections and observations on the society were appreciated with regard to key development sectors in the region such as water, agriculture; health; breeding, fishery. We concluded by giving some recommendations that might be very useful for policy/decision makers but also by listing possible topics for further research that could be addressed by the scientific community.

Keywords: Climate change; Climate models; Past Projections; Observations; West Africa

Modeling climate dynamics in a comprehensive way and improving its predictability in a warming world requires a better understanding of climate variability across scales. However, fundamental mechanisms governing variability on long timescales are still poorly understood.
The temporal evolution of climate can be inferred from paleoclimate records, such as ice cores or marine sediments. Power spectra serve to quantify changes of variability over time and to identify timescales associated with periodic or quasi-periodic processes. The spectra of surface temperature not only comprise spectral peaks, but also reveal a continuous part. It was shown that the background continuum exhibits a scale break, following different power-laws on monthly to decadal versus millennial to longer periods. It is yet mostly unexplained, how these power-laws arise and whether a coupling between different timescales can be deduced from it. We study these questions by comparing and applying spectral analyses to paleoclimate records and climate model simulations for the Quaternary. The data is used to reconstruct the temperature spectrum on diurnal to astronomical timescales. We extend previous studies by including climate responses, such as δ¹⁸O and temperature records, and climate forcings, for example, insolation and volcanic forcing. The emergence of scaling in temperature variability is analyzed by successively accessing the background continuum. Higher order spectra test for correlations between forcings and responses. In particular, the bispectrum and bicoherence is computed for statistical processes and evaluated for temperature records in order to study whether the scaling properties are related to energy transfers between different states in time. We elaborate the potential of these methods to reveal dynamical processes governing the continuous spectrum of surface temperature.