Pacific multidecadal (50–70 year) variability instigated by volcanic forcing during the Little Ice Age (1250–1850)

The Pacific decadal oscillation (PDO) is the leading mode of decadal climate variability over the North Pacific. However, it remains unknown to what extent external forcings can influence the PDO’s periodicity and magnitude over the past 2000 years. We show that the paleo-assimilation products (LMR) and proxy data suggest a 20–40 year PDO occurred during both the Mediaeval Climate Anomaly (MCA, ~ 750–1150) and Little Ice Age (LIA, ~ 1250–1850) while a salient 50–70 year variance peak emerged during the LIA. These results are reproduced well by the CESM simulations in the all-forcing (AF) and single volcanic forcing (Vol) experiments. We show that the 20–40 year PDO is an intrinsic mode caused by internal variability but the 50–70 year PDO during the LIA is a forced mode primarily shaped by volcanic forcing. The intrinsic mode develops in tandem with tropical ENSO-like anomalies, while the forced mode develops from the western Pacific and unrelated to tropical sea surface temperature anomalies. The volcanism-induced land–sea thermal contrast may trigger anomalous northerlies over the western North Pacific (WNP), leading to reduced northward heat transport and the cooling in the Kuroshio–Oyashio Extension (KOE), generating the forced mode. A 50–70 year Atlantic multidecadal oscillation founded during the LIA under volcanic forcing may also contribute to the forced mode. These findings shed light on the interplay between the internal variability and external forcing and the present and future changes of the PDO.

Hemispheric asymmetries in recent changes of the stratospheric circulation

Despite the expected opposite effects of ozone recovery, the stratospheric Brewer-Dobson circulation (BDC) has been found to weaken in the Northern hemisphere (NH) relative to the Southern hemisphere (SH) in recent decades, inducing substantial effects on chemical composition. We investigate hemispheric asymmetries in BDC changes since about 2000 in simulations with the transport model CLaMS driven with different reanalyses (ERA5, ERA-Interim, JRA-55, MERRA-2) and contrast those to a suite of free-running climate model simulations. We find that age of air increases robustly in the NH stratosphere relative to the SH in all reanalyses considered. Related nitrous oxide changes agree well between reanalysis-driven simulations and satellite measurements, providing observational evidence for the hemispheric asymmetry in BDC changes. Residual circulation metrics further show that the composition changes are caused by structural BDC changes related to an upward shift and strengthening of the deep BDC branch, resulting in longer transit times, and a downward shift and weakening shallow branch in the NH relative to the SH. All reanalyses agree on this mechanism. Although climate model simulations show that ozone recovery will lead to overall reduced circulation and age of air trends, the hemispherically asymmetric signal in circulation trends is small compared to internal variability. Therefore, the observed circulation trends over the recent past are not in contradiction to expectations from climate models. Furthermore, the hemispheric asymmetry in BDC trends imprints on the composition of the lower stratosphere and the signal might propagate into the troposphere, potentially affecting composition down to the surface.

Effect of Dairy, Season, and Sampling Position on Physical Properties of Trentingrana Cheese: Application of an LMM-ASCA Model

Trentingrana hard cheese is a geographic specification of the PDO Grana Padano. It is produced according to an internal regulation by many cooperative dairy factories in the Trentino region (northern Italy), using a semi-artisanal process (the only allowed ingredients are milk, salt, and rennet). Within the PSR project TRENTINGRANA, colorimetric and textural measurements have been collected from 317 cheese wheels, which were sampled bi-monthly from all the consortium dairies (n = 15) within the timeframe of two years, to estimate the effect on physical properties related to the season of the year and the dairy factory implant. To estimate the effect of the dairy and the time of the year, considering the internal variability of each cheese wheel, a linear mixed-effect model combined with a simultaneous component analysis (LMM-ASCA) is proposed. Results show that all the factors have a significant effect on the colorimetric and textural properties of the cheese. There are five clusters of dairies producing cheese with similar properties, three different couples of months of the year when the cheese produced is significantly different from all the others, and the effect of the geometry of the cheese wheel is reported as well.

Estimating the role of climate internal variability and source of uncertainties in hydrological climate-impact projections

Hydrological climate-impact projections in future are limited by large uncertainties from various sources. Therefore, this study aimed to explore and estimate the sources of uncertainties involved in climate changing impacted assessment in a representative watershed of Northeastern China. Moreover, recent researches indicated that the climate internal variability (CIV) plays an important role in various of hydrological climate-impact projections. Six downscaled Global climate models (GCMs) under two emission scenarios and a calibrate Soil and Water Assessment Tool (SWAT) model were used to obtain hydrological projections in future periods. The CIV and signal-to-noise ratio (SNR) are investigated to analyze the the role of internal variability in hydrological projections. The results shows that the internal variability shows a considerable influence on hydrological projections, which need be partitioned and quantified particularly. Moreover, it worth noting the CIV can propagate from precipitation and ET to runoff projections through the hydrological simulation process. In order to partition the CIV and sources of uncertainties, the uncertainty decomposed frameworks based on analysis of variance (ANOVA) are established. The results demonstrate that the CIV and GCMs are the dominate contributors of runoff in rainy season. In contrast, the CIV and SWAT model parameter sets provided obvious uncertainty to runoff in January to May and October to December. The findings of this study advised that the uncertainty is complex in hydrological simulation process hence, it is meaning and necessary to estimate the uncertainty in climate simulation process, the uncertainty analysis results can provide effectively efforts to reduce uncertainty and then give some positive suggestions to stakeholders for adaption countermeasure under climate change.

How extreme could the near term evolution of the Indian Summer Monsoon rainfall be?

We provide a methodology to estimate possible extreme changes in seasonal rainfall for the coming decades. We demonstrate this methodology using Indian summer monsoon rainfall as an example however it can be extended to other climate variables, regions and timescale conditional to the model forecasts being a good representative of the observations in current climate. We use an ensemble of ~1600 initialized climate simulations from selected seasonal prediction systems to estimate internal variability and how it can exacerbate or alleviate forced climate change. Our estimates show that for the next decade there is a ~60% chance of wetting trends whereas the chance of drying is ~40%. Wetting trends are systematically more favoured than drying with increasing length of the period. This provides a quantitative explanation for the varying trends in the past observational record of rainfall over India. We also quantify the likelihood of extreme trends and show that there is at least a 1% chance that monsoon rainfall could increase or decrease by one fifth over the next decade and that more extreme trends, though unlikely, are possible. We find that monsoon rainfall trends are influenced by trends in sea-surface temperatures over the Niño3.4 region and tropical Indian Ocean, and ~1.5° cooling or warming of these regions can approximately double or negate the influence of climate change on rainfall over the next two decades. We also investigate the time-of-emergence of climate change signals in rainfall trends and find that it is unlikely for a climate change signal to emerge by the year 2050 due to the large internal variability of monsoon rainfall. The estimates of extreme rainfall change provided here could be useful for governments to prepare for worst-case scenarios and therefore aid disaster preparedness and decision-making.

Global and amplified land warming trends over the past 40+ years are entirely human-driven.

<p class="p1">The role of external (radiative) forcing factors and internal unforced (ocean) low-frequency variations in the instrumental global temperature record are still hotly debated. More recent findings point towards a larger contribution from changes in external forcing, but the jury is still out. While the estimation of the human-induced total global warming fraction since pre-industrial times is fairly robust and mostly independent of multidecadal internal variability, this is not necessarily the case for key regional features such as Arctic amplification or enhanced warming over continental land areas. Accounting for the slow global temperature adjustment after strong volcanic eruptions, the spatially heterogeneous nature of anthropogenic aerosol forcing and known biases in the sea surface temperature record, almost all of the multidecadal fluctuations observed over at least the last 160+ years can be explained without a relevant role for internal variability. Using a two-box response model framework, I will demonstrate that not only multidecadal variability is very likely a forced response, but warming trends over the past 40+ years are entirely attributable to human factors. Repercussions for amplifed European (or D-A-CH for that matter) warming and associated implications for extreme weather events are discussed. Further consideration is given to the communications aspect of such critical results as well as the question of wider societal impacts.</p>

Sensitivity of the middle and upper atmospheric dynamics to the modification of the gravity wave drag parameterization in ICON model

<p>The gravity waves play a crucial role in driving and shaping the middle atmospheric circulation. The Upper-Atmospheric extension of the ICOsahedral Non-hydrostatic (UA-ICON) general circulation model was recently developed with satisfying performances in both idealized test cases and climate simulations, however the sensitivity of the circulation to the parameterized orographic and non-orographic gravity wave drag remains largely unexplored. Using UA-ICON and ICON-NWP, the sensitivity of the dynamics and circulation to both orographic and non-orographic parameterized gravity waves effects are investigated. ICON-NWP stands for the numerical-weather prediction mode of the ICON model (see Zängl et al, 2015, QJRMetSoc), with a model top at about 80 km altitude. The UA-ICON mode differs from ICON-NWP in deep-atmosphere dynamics (instead of shallow-atmosphere dynamics) and upper-atmosphere physics parameterizations being switched on. In addition, the model top is at about 150 km.</p> <p>The sensitivity experiments involve employing repeated annual cycle sea surface temperatures, sea ice, and greenhouse gases under year 1988. This year is selected as both El-Nino southern oscillation and pacific decadal oscillation are in their neutral phase and no explosive volcano eruption has occurred and hence conditions in this year can serve as a useful proxy for the multi-year mean condition and an estimate of its internal variability. For both UA-ICON and ICON-NWP, we perform simulations where in the control (CTL) simulation both orographic and non-orographic gravity wave drags are switched on. The other two experiments are identical to the control simulation except that either orographic (OGWD-off) or b) non-orographic (NGWD-off) gravity wave drags are switched off. The analysis include comparisons between CTL and OGWD-off and NGWD-off simulations and include wave-mean flow interaction diagnostics (Eliassen-Palm flux and its divergence and refractive index of Rossby waves) and mass stream function of the Brewer-Dobson circulation. We also investigate the sudden stratospheric warming frequency and polar vortex morphology in order to understand whether a missing gravity wave forcing can further amplify or curtail the effects of future climate. We present our goal, method as well as first results and discuss possible further analysis. </p>

Roles of meridional overturning in subpolar Southern Ocean SST trends: Insights from ensemble simulations

One of the most puzzling observed features of recent climate has been a multidecadal surface cooling trend over the subpolar Southern Ocean (SO). In this study we use large ensembles of simulations with multiple climate models to study the role of the SO meridional overturning circulation (MOC) in these sea surface temperature (SST) trends. We find that multiple competing processes play prominent roles, consistent with multiple mechanisms proposed in the literature for the observed cooling. Early in the simulations (20th century and early 21st century) internal variability of the MOC can have a large impact, in part due to substantial simulated multidecadal variability of the MOC. Ensemble members with initially strong convection (and related surface warming due to convective mixing of subsurface warmth to the surface) tend to subsequently cool at the surface as convection associated with internal variability weakens. A second process occurs in the late 20th and 21st centuries, as weakening of oceanic convection associated with global warming and high latitude freshening can contribute to the surface cooling trend by suppressing convection and associated vertical mixing of subsurface heat. As the simulations progress, the multidecadal SO variability is suppressed due to forced changes in the mean state and increased oceanic stratification. As a third process, the shallower mixed layers can then rapidly warm due to increasing forcing from greenhouse gas warming. Also, during this period the ensemble spread of SO SST trend partly arises from the spread of the wind-driven Deacon cell strength. Thus, different processes could conceivably have led to the observed cooling trend, consistent with the range of possibilities presented in the literature. To better understand the causes of the observed trend it is important to better understand the characteristics of internal low-frequency variability in the SO and the response of that variability to global warming.

Extreme metrics from large ensembles: investigating the effects of ensemble size on their estimates

We consider the problem of estimating the ensemble sizes required to characterize the forced component and the internal variability of a number of extreme metrics. While we exploit existing large ensembles, our perspective is that of a modeling center wanting to estimate a priori such sizes on the basis of an existing small ensemble (we assume the availability of only five members here). We therefore ask if such a small-size ensemble is sufficient to estimate accurately the population variance (i.e., the ensemble internal variability) and then apply a well-established formula that quantifies the expected error in the estimation of the population mean (i.e., the forced component) as a function of the sample size n, here taken to mean the ensemble size. We find that indeed we can anticipate errors in the estimation of the forced component for temperature and precipitation extremes as a function of n by plugging into the formula an estimate of the population variance derived on the basis of five members. For a range of spatial and temporal scales, forcing levels (we use simulations under Representative Concentration Pathway 8.5) and two models considered here as our proof of concept, it appears that an ensemble size of 20 or 25 members can provide estimates of the forced component for the extreme metrics considered that remain within small absolute and percentage errors. Additional members beyond 20 or 25 add only marginal precision to the estimate, and this remains true when statistical inference through extreme value analysis is used. We then ask about the ensemble size required to estimate the ensemble variance (a measure of internal variability) along the length of the simulation and – importantly – about the ensemble size required to detect significant changes in such variance along the simulation with increased external forcings. Using the F test, we find that estimates on the basis of only 5 or 10 ensemble members accurately represent the full ensemble variance even when the analysis is conducted at the grid-point scale. The detection of changes in the variance when comparing different times along the simulation, especially for the precipitation-based metrics, requires larger sizes but not larger than 15 or 20 members. While we recognize that there will always exist applications and metric definitions requiring larger statistical power and therefore ensemble sizes, our results suggest that for a wide range of analysis targets and scales an effective estimate of both forced component and internal variability can be achieved with sizes below 30 members. This invites consideration of the possibility of exploring additional sources of uncertainty, such as physics parameter settings, when designing ensemble simulations.