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The aim of this work was to establish the response of growing sheep, goats and cattle to different nutritional environments. Data from 590 publications representing 2225 treatments were analysed. The results showed that each 10% increase in NDF was accompanied by 0.11 g/kg live weight (LW) and 0.32 g/kg metabolic live weight (LW0.75) decreases in DMI. Otherwise, the response of DMI to CP (CP%DM) content was curvilinear (P<0.01), without any significant difference in the slope between species. The percentage of concentrate (% CC) affected DMI curvilinearly, without any significant difference between species. This meta-analysis demonstrated the negative linear effect of NDF and the quadratic effect of CP concentration on organic matter digestibility (OMd). For growth performance, the three species responded curvilinearly to variations in metabolisable energy intake (MEI MJ/kg LW0.75) and digestible CP (DCPI g/kg LW0.75) intake (P<0.01). At the same level of MEI, average daily gain (ADG) varied with CP contents of the diet, and only the intercept differences were significant between the three levels (P=0.07). At the same level of DCPI, ADG varied with energy level (below maintenance (LE−−), 1 to 1.2×maintenance (LE−), 1.2 to 1.4× maintenance (ME+−), and >1.4, corresponding to maximum growth (HE+)). No significant difference was observed between LE−− and LE−, and no significant difference was observed between ME+− and HE+. For nitrogen balance, no difference was observed between species for a given level of nitrogen intake.

Tropical cyclones (TCs, also known as hurricanes and typhoons) generally form at low latitudes with access to the warm waters of the tropical oceans, but far enough off the equator to allow planetary rotation to cause aggregating convection to spin up into coherent vortices. Yet, current prognostic frameworks for TC latitudes make contradictory predictions for climate change. Simulations of past warm climates, such as the Eocene and Pliocene, show that TCs can form and intensify at higher latitudes than of those during pre-industrial conditions. Observations and model projections for the twenty-first century indicate that TCs may again migrate poleward in response to anthropogenic greenhouse gas emissions, which poses profound risks to the planet’s most populous regions. Previous studies largely neglected the complex processes that occur at temporal and spatial scales of individual storms as these are poorly resolved in numerical models. Here we review this mesoscale physics in the context of responses to climate warming of the Hadley circulation, jet streams and Intertropical Convergence Zone. We conclude that twenty-first century TCs will most probably occupy a broader range of latitudes than those of the past 3 million years as low-latitude genesis will be supplemented with increasing mid-latitude TC favourability, although precise estimates for future migration remain beyond current methodologies. Hurricanes and typhoons are tracking further poleward due to the effects of climate change, according to a synthesis of numerical modelling results, observations and palaeoclimate records.

Soil carbon release remains a highly uncertain climate feedback. Research now shows that the temperature control on carbon turnover is more sensitive in cold climates, supporting projections of a strong carbon–climate feedback from northern soils. The projected loss of soil carbon to the atmosphere resulting from climate change is a potentially large but highly uncertain feedback to warming. The magnitude of this feedback is poorly constrained by observations and theory, and is disparately represented in Earth system models (ESMs) 1 , 2 , 3 . To assess the climatological temperature sensitivity of soil carbon, we calculate apparent soil carbon turnover times 4 that reflect long-term and broad-scale rates of decomposition. Here, we show that the climatological temperature control on carbon turnover in the top metre of global soils is more sensitive in cold climates than in warm climates and argue that it is critical to capture this emergent ecosystem property in global-scale models. We present a simplified model that explains the observed high cold-climate sensitivity using only the physical scaling of soil freeze–thaw state across climate gradients. Current ESMs fail to capture this pattern, except in an ESM that explicitly resolves vertical gradients in soil climate and carbon turnover. An observed weak tropical temperature sensitivity emerges in a different model that explicitly resolves mineralogical control on decomposition. These results support projections of strong carbon–climate feedbacks from northern soils 5 , 6 and demonstrate a method for ESMs to capture this emergent behaviour.

Accurate estimates of past global mean surface temperature (GMST) help to contextualise future climate change and are required to estimate the sensitivity of the climate system to CO2 forcing through Earth's history. Previous GMST estimates for the latest Paleocene and early Eocene (∼57 to 48 million years ago) span a wide range (∼9 to 23 °C higher than pre-industrial) and prevent an accurate assessment of climate sensitivity during this extreme greenhouse climate interval. Using the most recent data compilations, we employ a multi-method experimental framework to calculate GMST during the three DeepMIP target intervals: (1) the latest Paleocene (∼57 Ma), (2) the Paleocene–Eocene Thermal Maximum (PETM; 56 Ma), and (3) the early Eocene Climatic Optimum (EECO; 53.3 to 49.1 Ma). Using six different methodologies, we find that the average GMST estimate (66 % confidence) during the latest Paleocene, PETM, and EECO was 26.3 °C (22.3 to 28.3 °C), 31.6 °C (27.2 to 34.5 °C), and 27.0 °C (23.2 to 29.7 °C), respectively. GMST estimates from the EECO are ∼10 to 16 °C warmer than pre-industrial, higher than the estimate given by the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (9 to 14 °C higher than pre-industrial). Leveraging the large “signal” associated with these extreme warm climates, we combine estimates of GMST and CO2 from the latest Paleocene, PETM, and EECO to calculate gross estimates of the average climate sensitivity between the early Paleogene and today. We demonstrate that “bulk” equilibrium climate sensitivity (ECS; 66 % confidence) during the latest Paleocene, PETM, and EECO is 4.5 °C (2.4 to 6.8 °C), 3.6 °C (2.3 to 4.7 °C), and 3.1 °C (1.8 to 4.4 °C) per doubling of CO2. These values are generally similar to those assessed by the IPCC (1.5 to 4.5 °C per doubling CO2) but appear incompatible with low ECS values (<1.5 per doubling CO2).

Potential evapotranspiration (PET) is a supply-independent measure of the evaporative demand of a terrestrial climate—of basic importance in climatology, hydrology, and agriculture. Future increases in PET from greenhouse warming are often cited as key drivers of global trends toward drought and aridity. The present work computes recent and “business as usual” future Penman–Monteith PET fields at 3-hourly resolution in 13 modern global climate models. The percentage change in local annual-mean PET over the upcoming century is almost always positive, modally low double-digit in magnitude, usually increasing with latitude, yet quite divergent between models. These patterns are understood as follows. In every model, the global field of PET percentage change is found to be dominated by the direct, positive effects of constant-relative-humidity warming (via increasing vapor deficit and increasing Clausius–Clapeyron slope). This direct-warming term accurately scales as the PET-weighted (warm-season daytime) local warming, times 5%–6% °C−1(related to the Clausius–Clapeyron equation), times an analytic factor ranging from about 0.25 in warm climates to 0.75 in cold climates, plus a small correction. With warming of several degrees, this product is of low double-digit magnitude, and the strong temperature dependence gives the latitude dependence. Similarly, the intermodel spread in the amount of warming gives most of the spread in this term. Additional spread in the total change comes from strong disagreement on radiation, relative humidity, and wind speed changes, which make smaller yet substantial contributions to the full PET percentage change fields.

Palaeoclimate reconstructions of periods with warm climates and high atmospheric CO 2 concentrations are crucial for developing better projections of future climate change. Deep-ocean 1 , 2 and high-latitude 3 palaeotemperature proxies demonstrate that the Eocene epoch (56 to 34 million years ago) encompasses the warmest interval of the past 66 million years, followed by cooling towards the eventual establishment of ice caps on Antarctica. Eocene polar warmth is well established, so the main obstacle in quantifying the evolution of key climate parameters, such as global average temperature change and its polar amplification, is the lack of continuous high-quality tropical temperature reconstructions. Here we present a continuous Eocene equatorial sea surface temperature record, based on biomarker palaeothermometry applied on Atlantic Ocean sediments. We combine this record with the sparse existing data 4 , 5 – 6 to construct a 26-million-year multi-proxy, multi-site stack of Eocene tropical climate evolution. We find that tropical and deep-ocean temperatures changed in parallel, under the influence of both long-term climate trends and short-lived events. This is consistent with the hypothesis that greenhouse gas forcing 7 , 8 , rather than changes in ocean circulation 9 , 10 , was the main driver of Eocene climate. Moreover, we observe a strong linear relationship between tropical and deep-ocean temperatures, which implies a constant polar amplification factor throughout the generally ice-free Eocene. Quantitative comparison with fully coupled climate model simulations indicates that global average temperatures were about 29, 26, 23 and 19 degrees Celsius in the early, early middle, late middle and late Eocene, respectively, compared to the preindustrial temperature of 14.4 degrees Celsius. Finally, combining proxy- and model-based temperature estimates with available CO 2 reconstructions 8 yields estimates of an Eocene Earth system sensitivity of 0.9 to 2.3 kelvin per watt per square metre at 68 per cent probability, consistent with the high end of previous estimates 11 . A 26-million-year record of equatorial sea surface temperatures reveals synchronous changes of tropical and polar temperatures during the Eocene epoch forced by variations in concentrations of atmospheric carbon dioxide, with a constant degree of polar amplification.

The parameterization of moist convection contributes to uncertainty in climate modeling and numerical weather prediction. Machine learning (ML) can be used to learn new parameterizations directly from high‐resolution model output, but it remains poorly understood how such parameterizations behave when fully coupled in a general circulation model (GCM) and whether they are useful for simulations of climate change or extreme events. Here we focus on these issues using idealized tests in which an ML‐based parameterization is trained on output from a conventional parameterization and its performance is assessed in simulations with a GCM. We use an ensemble of decision trees (random forest) as the ML algorithm, and this has the advantage that it automatically ensures conservation of energy and nonnegativity of surface precipitation. The GCM with the ML convective parameterization runs stably and accurately captures important climate statistics including precipitation extremes without the need for special training on extremes. Climate change between a control climate and a warm climate is not captured if the ML parameterization is only trained on the control climate, but it is captured if the training includes samples from both climates. Remarkably, climate change is also captured when training only on the warm climate, and this is because the extratropics of the warm climate provides training samples for the tropics of the control climate. In addition to being potentially useful for the simulation of climate, we show that ML parameterizations can be interrogated to provide diagnostics of the interaction between convection and the large‐scale environment. Plain Language Summary Small‐scale features such as clouds are typically represented in climate models by simplified physical models, and these simplified models introduce errors and uncertainties. A promising alternative approach is to use machine learning to train a statistical model to represent small‐scale processes based on output from expensive physics‐based models that better represent the small‐scale processes. Here we use idealized tests to explore the implications of incorporating a machine‐learning model of atmospheric convection in a climate model. We find that such an approach can give accurate simulations of mean climate and heavy rainfall events. The machine‐learning model does not work well for global warming if it is only trained on the current climate. However, it does work well for global warming if trained on both the current and warmer climates, and it works surprisingly well if only trained on the warmer climate. We also show that the machine‐learning model can be used to better understand the underlying physical processes. Key Points Random‐forest parameterization of convection gives accurate GCM simulations of climate and precipitation extremes in idealized tests Climate change captured when trained on control and warm climate, or only on warm climate, but not when trained only on control climate Machine‐learning parameterizations can also be interrogated to generate diagnostics of interaction of convection with the environment

Benthic oxygen isotope records are commonly used as a proxy for global mean surface temperatures during the Late Cretaceous and Cenozoic, and the resulting estimates have been extensively used in characterizing major trends and transitions in the climate system and for analysing past climate sensitivity. However, some fundamental assumptions governing this proxy have rarely been tested. Two key assumptions are (a) benthic foraminiferal temperatures are geographically well mixed and are linked to surface high-latitude temperatures, and (b) surface high-latitude temperatures are well correlated with global mean temperatures. To investigate the robustness of these assumptions through geological time, we performed a series of 109 climate model simulations using a unique set of paleogeographical reconstructions covering the entire Phanerozoic at the stage level. The simulations have been run for at least 5000 model years to ensure that the deep ocean is in dynamic equilibrium. We find that the correlation between deep ocean temperatures and global mean surface temperatures is good for the Cenozoic, and thus the proxy data are reliable indicators for this time period, albeit with a standard error of 2 K. This uncertainty has not normally been assessed and needs to be combined with other sources of uncertainty when, for instance, estimating climate sensitivity based on using δ18O measurements from benthic foraminifera. The correlation between deep and global mean surface temperature becomes weaker for pre-Cenozoic time periods (when the paleogeography is significantly different from the present day). The reasons for the weaker correlation include variability in the source region of the deep water (varying hemispheres but also varying latitudes of sinking), the depth of ocean overturning (some extreme warm climates have relatively shallow and sluggish circulations weakening the link between the surface and deep ocean), and the extent of polar amplification (e.g. ice albedo feedbacks). Deep ocean sediments prior to the Cretaceous are rare, so extending the benthic foraminifera proxy further into deeper time is problematic, but the model results presented here would suggest that the deep ocean temperatures from such time periods would probably be an unreliable indicator of global mean surface conditions.

The North Atlantic Oscillation and the Arctic Oscillation are modes of climate variability affecting temperature and precipitation in the mid-latitudes. Here we use reanalysis data and climate model simulations of historical and warm climates to show that the relationship between the two oscillations changes with climate warming. The two modes are currently highly correlated, as both are strongly influenced by the downward propagation of stratospheric polar vortex anomalies into the troposphere. When considering a very warm climate scenario, the hemispherically defined Arctic Oscillation pattern shifts to reflect variability of the North Pacific storm track, while the regionally defined North Atlantic Oscillation pattern remains stable. The stratosphere remains an important precursor for North Atlantic Oscillation, and surface Eurasian and Aleutian pressure anomalies precede stratospheric anomalies. Idealized general circulation model simulations suggest that these modifications are linked to the stronger warming of the Pacific compared with the slower warming of the Atlantic Ocean.The Arctic Oscillation and North Atlantic Oscillation are modes of Northern Hemisphere climate variability with high temporal and spatial correlation. With strong warming, climate models suggest their link breaks down due to a divergent response to the Pacific and Atlantic oceans and stratosphere.

With global warming, extreme weather frequently and severely appears globally. Extreme precipitation is one of the extreme weather events that can cause many natural disasters, such as floods and waterlogging. In this study, Global Precipitation Climatology Project (GPCP) daily precipitation data were used to investigate extreme precipitation and its contribution to annual precipitation in different global climate regions and typical river basins. The climate types included equatorial climates (EC), arid climates (AC), warm temperate climates (WTC), snowy climates (SC) and polar climates (PC). R99p, Rx5day, CWD and R20 was selected as extreme precipitation indices in this study; extreme precipitation days were defined by CWD and R20. The results showed that EC and WTC had higher extreme precipitation level; SC and PC had lower extreme precipitation amounts and days than AC. R99p, Rx5day and CWD monitored higher extreme precipitation contribution degrees in AC; however, R20 monitored higher contribution degrees in EC and WTC. R99p, Rx5day and CWD showed higher extreme precipitation contribution degrees in North Africa, the Middle East, Australia and northwestern China; R20 showed higher contribution degrees in South America, the southeastern United States and South Asia. Based on historical observational data, Heilongjiang Basin (HB), Yellow River Basin (YERB), Yangtze River Basin (YARB), Ganges River Basin (GRB), Danube River Basin (DRB) and Mekong River Basin (MERB) had high-frequency extreme precipitation in summer. The research results are helpful for understanding the characteristics of extreme precipitation and provide a reference for flood control and disaster reduction in different climatic regions and main river basins.