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Abstract The role and impact that boundary layer and shallow cumulus clouds have on the medium-range forecast of a large-scale weather system is discussed in this study. A mesoscale version of the Global Environmental Multiscale (GEM) atmospheric model is used to produce a 5-day numerical forecast of a midlatitude large-scale weather system that occurred over the Pacific Ocean during February 2003. In this version of GEM, four different schemes are used to represent (i) boundary layer clouds (including stratus, stratocumulus, and small cumulus clouds), (ii) shallow cumulus clouds (overshooting cumulus), (iii) deep convection, and (iv) nonconvective clouds. Two of these schemes, that is, the so-called MoisTKE and Kuo Transient schemes for boundary layer and overshooting cumulus clouds, respectively, have been recently introduced in GEM and are described in more detail. The results show that GEM, with this new cloud package, is able to represent the wide variety of clouds observed in association with the large-scale weather system. In particular, it is found that the Kuo Transient scheme is mostly responsible for the shallow/intermediate cumulus clouds in the rear portion of the large-scale system, whereas MoisTKE produces the low-level stratocumulus clouds ahead of the system. Several diagnostics for the rear portion of the system reveal that the role of MoisTKE is mainly to increase the vertical transport (diffusion) associated with the boundary layer clouds, while Kuo Transient is acting in a manner more consistent with convective stabilization. As a consequence, MoisTKE is not able to remove the low-level shallow cloud layer that is incorrectly produced by the GEM nonconvective condensation scheme. Kuo Transient, in contrast, led to a significant reduction of these nonconvective clouds, in better agreement with satellite observations. This improved representation of stratocumulus and cumulus clouds does not have a large impact on the overall sea level pressure patterns of the large-scale weather system. Precipitation in the rear portion of the system, however, is found to be smoother when MoisTKE is used, and significantly less when the Kuo Transient scheme is switched on.

Abstract Reconstructions of ocean primary productivity (PP) help to explain past and present biogeochemical cycles and climate changes in the oceans. We document PP variations over the last 50 kyr in a currently oligotrophic subtropical region, the Gulf of Cadiz. Data combine refined results from previous investigations on dinocyst assemblages, alkenones, and stable isotopes ( 18 O, 13 C) in planktonic ( Globigerina bulloides ) and endobenthic ( Uvigerina mediterranea ) foraminifera from cores MD04‐2805 CQ and MD99‐2339, with new isotopic measurements on epibenthic ( Cibicides pachyderma ‐ Cibicidoides wuellerstorfi ) foraminifera and dinocyst‐based estimates of PP using the new n  = 1,968 modern database. We constrain PP variations and export production by integrating qualitative information from bioindicators with dinocyst‐based quantitative reconstructions such as PP and seasonal sea surface temperature and information about remineralization from the benthic Δδ 13 C (difference between epibenthic and endobenthic foraminiferal δ 13 C signatures). This study also includes new information on alkenone‐based SST and total organic carbon which provides insights into the relationship between past regional hydrological activity and PP regime change. We show that PP, carbon export, and remineralization were generally high in the NE subtropical Atlantic Ocean during the last glacial period and that the Last Glacial Maximum (LGM) had lower Δδ 13 C than the Heinrich Stadials with sustained high PP, likely allowing enhanced carbon sequestration. We link these PP periods to the dynamics of upwelling, active almost year‐round during sadials, but restricted to spring‐summer during interstadials and LGM, like today. During interstadials, nutrient advection through freshwater inputs during autumn‐winter needs also to be considered to fully understand PP regimes. , Key Points Productivity (PP) in the Gulf of Cadiz is dependent on the seasonality control for both upwelling and nutrient‐enriched freshwater inputs We show generally high PP, carbon export, and remineralization during the last glacial period at the study site The Last Glacial Maximum had lower Δδ 13 C than the Heinrich Stadials with sustained high PP likely allowing enhanced carbon sequestration

Abstract Climate changes over the past two millennia in the central part of the Gulf of St. Lawrence are documented in this paper with the aim of determining and understanding the natural climate variability and the impact of anthropogenic forcing at a regional scale. The palynological content (dinocysts, pollen, and spores) of the composite marine sediment core MSM46-03 collected in the Laurentian Channel was used to reconstruct oceanographic and climatic changes with a multidecadal temporal resolution. Sea-surface conditions, including summer salinity and temperature, sea-ice cover, and primary productivity, were reconstructed from dinocyst assemblages. Results revealed a remarkable cooling trend of about 4°C after 1230 cal yr BP (720 CE) and a culmination with a cold pulse dated to 170–40 cal yr BP (1780–1910 CE), which likely corresponds to the regional signal of the Little Ice Age. This cold interval was followed by a rapid warming of about 3°C. In the pollen assemblages, the decrease of Pinus abundance over the past 1700 yr suggests changes in wind regimes, likely resulting from increased southerly incursions of cold and dry Arctic air masses into southeastern Canada.

Abstract Declining sea ice is expected to change the Arctic's physical and biological systems in ways that are difficult to predict. This study used stable isotope compositions (δ 13 C and δ 15 N) of archaeological, historic, and modern Pacific walrus ( Odobenus rosmarus divergens ) bone collagen to investigate the impacts of changing sea ice conditions on walrus diet during the last ~4000 yr. An index of past sea ice conditions was generated using dinocyst-based reconstructions from three locations in the northeastern Chukchi Sea. Archaeological walrus samples were assigned to intervals of high and low sea ice, and δ 13 C and δ 15 N were compared across ice states. Mean δ 13 C and δ 15 N values were similar for archaeological walruses from intervals of high and low sea ice; however, variability among walruses was greater during low-ice intervals, possibly indicating decreased availability of preferred prey. Overall, sea ice conditions were not a primary driver of changes in walrus diet. The diet of modern walruses was not consistent with archaeological low sea ice intervals. Rather, the low average trophic position of modern walruses (primarily driven by males), with little variability among individuals, suggests that trophic changes to this Arctic ecosystem are still underway or are unprecedented in the last ~4000 yr.

Abstract An important source of model uncertainty in climate models arises from unconfined model parameters in physical parameterizations. These parameters are commonly estimated on the basis of manual adjustments (expert tuning), which carries the risk of overtuning the parameters for a specific climate region or time period. This issue is particularly germane in the case of regional climate models (RCMs), which are often developed and used in one or a few geographical regions only. This study addresses the role of objective parameter calibration in this context. Using a previously developed objective calibration methodology, an RCM is calibrated over two regions (Europe and North America) and is used to investigate the transferability of the results. A total of eight different model parameters are calibrated, using a metamodel to account for parameter interactions. The study demonstrates that the calibration is effective in reducing model biases in both domains. For Europe, this concerns in particular a pronounced reduction of the summer warm bias and the associated overestimation of interannual temperature variability that have persisted through previous expert tuning efforts and are common in many global and regional climate models. The key process responsible for this improvement is an increased hydraulic conductivity. Higher hydraulic conductivity increases the water availability at the land surface and leads to increased evaporative cooling, stronger low cloud formation, and associated reduced incoming shortwave radiation. The calibrated parameter values are found to be almost identical for both domains; that is, the parameter calibration is transferable between the two regions. This is a promising result and indicates that models may be more universal than previously considered.

Abstract Postglacial changes in sea-surface conditions, including sea-ice cover, summer temperature, salinity, and productivity were reconstructed from the analyses of dinocyst assemblages in core S2528 collected in the northwestern Barents Sea. The results show glaciomarine-type conditions until about 11,300 ± 300 cal yr BP and limited influence of Atlantic water at the surface into the Barents Sea possibly due to the proximity of the Svalbard-Barents Sea ice sheet. This was followed by a transitional period generally characterized by cold conditions with dense sea-ice cover and low-salinity pulses likely related to episodic freshwater or meltwater discharge, which lasted until 8700 ± 700 cal yr BP. The onset of “interglacial” conditions in surface waters was marked by a major change in dinocyst assemblages, from dominant heterotrophic to dominant phototrophic taxa. Until 4100 ± 150 cal yr BP, however, sea-surface conditions remained cold, while sea-surface salinity and sea-ice cover recorded large amplitude variations. By ~4000 cal yr BP optimum sea-surface temperature of up to 4°C in summer and maximum salinity of ~34 psu suggest enhanced influence of Atlantic water, and productivity reached up to 150 gC/m 2 /yr. After 2200 ± 1300 cal yr BP, a distinct cooling trend accompanied by sea-ice spreading characterized surface waters. Hence, during the Holocene, with exception of an interval spanning about 4000 to 2000 cal yr BP, the northern Barents Sea experienced harsh environments, relatively low productivity, and unstable conditions probably unsuitable for human settlements.

Abstract Digital leaf physiognomy (DLP) is considered as one of the most promising methods for estimating past climate. However, current models built using the DLP data set still lack precision, especially for mean annual precipitation (MAP). To improve predictive power, we developed five machine learning (ML) models for mean annual temperature (MAT) and MAP respectively, and then tested the precision of these models and some of their averaging compared with that obtained from other models. The precision of all models was assessed using a repeated stratified 10‐fold cross‐validation. For MAT, three combinations of models ( R 2 = .77) presented moderate improvements in precision over the multiple linear regression (MLR) model ( R 2 = .68). For log e (MAP), the averaging of the support vector machine (SVM) and boosting models improved the R 2 from .19 to .63 compared with that of the MLR model. For MAP, the R 2 of this model combination was 0.49, which was much better than that of the artificial neural network (ANN) model ( R 2 = .21). Even the bagging model, which had the lowest R 2 (.37) for log e (MAP), demonstrated better precision ( R 2 = .27) for MAP. Our palaeoclimate estimates for nine fossil floras were also more accurate, because they were in better agreement with independent paleoclimate evidence. Our study confirms that our ML models and their averaging can improve paleoclimatic reconstructions, providing a better understanding of the relationship between climate and leaf physiognomy.

Summary For decades, researchers have thought it was difficult to remove the uncertainty from the estimates of forest carbon storage and its changes on national sales. This is not only because of stochasticity in the data but also the bias to overcome in the computations. Most studies of the estimation, however, ignore quantitative analyses for the latter uncertainty. This bias primarily results from the widely used volume‐biomass method via scaling up forest biomass from limited sample plots to large areas. This paper addresses (i) the mechanism of scaling‐up error occurrence, and (ii) the quantitative effects of the statistical factors on the error. The error compensators were derived, and expressed by ternary functions with three variables: expectation, variance and the power in the volume‐biomass equation. This is based on analysing the effect of power‐law function convexity on scaling‐up error by solving the difference of both sides of the weighted Jensen inequality. The simulated data and the national forest inventory of China were used for algorithm testing and application, respectively. Scaling‐up error occurrence stems primarily from an effect of the distribution heterogeneity of volume density on the total biomass amount, and secondarily from the extent of function nonlinearities. In our experiments, on average 94·2% of scaling‐up error can be reduced for the statistical populations of forest stands in a region. China's forest biomass carbon was estimated as approximately 6·0 PgC or less at the beginning of the 2010s after on average 1·1% error compensation. The results of both the simulated data experiment and national‐scale estimation suggest that the biomass is overestimated for young forests more than others. It implies a necessity to compensate scaling‐up error, especially for the areas going through extensive afforestation and reforestation in past decades. This study highlights the importance of understanding how both the function nonlinearity and the statistics of the variables quantitatively affect the scaling‐up error. Generally, the presented methods will help to translate fine‐scale ecological relationships to estimate coarser scale ecosystem properties by correcting aggregation errors.

Abstract. A far infrared radiometer (FIRR) dedicated to measuring radiation emitted by clear and cloudy atmospheres was developed in the framework of the Thin Ice Clouds in Far InfraRed Experiment (TICFIRE) technology demonstration satellite project. The FIRR detector is an array of 80 × 60 uncooled microbolometers coated with gold black to enhance the absorptivity and responsivity. A filter wheel is used to select atmospheric radiation in nine spectral bands ranging from 8 to 50 µm. Calibrated radiances are obtained using two well-calibrated blackbodies. Images are acquired at a frame rate of 120 Hz, and temporally averaged to reduce electronic noise. A complete measurement sequence takes about 120 s. With a field of view of 6°, the FIRR is not intended to be an imager. Hence spatial average is computed over 193 illuminated pixels to increase the signal-to-noise ratio and consequently the detector resolution. This results in an improvement by a factor of 5 compared to individual pixel measurements. Another threefold increase in resolution is obtained using 193 non-illuminated pixels to remove correlated electronic noise, leading an overall resolution of approximately 0.015 W m−2 sr−1. Laboratory measurements performed on well-known targets suggest an absolute accuracy close to 0.02 W m−2 sr−1, which ensures atmospheric radiance is retrieved with an accuracy better than 1 %. Preliminary in situ experiments performed from the ground in winter and in summer on clear and cloudy atmospheres are compared to radiative transfer simulations. They point out the FIRR ability to detect clouds and changes in relative humidity of a few percent in various atmospheric conditions, paving the way for the development of new algorithms dedicated to ice cloud characterization and water vapor retrieval.

Abstract. The first airborne measurements of the Far-InfraRed Radiometer (FIRR) were performed in April 2015 during the panarctic NETCARE campaign. Vertical profiles of spectral upwelling radiance in the range 8–50 µm were measured in clear and cloudy conditions from the surface up to 6 km. The clear sky profiles highlight the strong dependence of radiative fluxes to the temperature inversion typical of the Arctic. Measurements acquired for total column water vapour from 1.5 to 10.5 mm also underline the sensitivity of the far-infrared greenhouse effect to specific humidity. The cloudy cases show that optically thin ice clouds increase the cooling rate of the atmosphere, making them important pieces of the Arctic energy balance. One such cloud exhibited a very complex spatial structure, characterized by large horizontal heterogeneities at the kilometre scale. This emphasizes the difficulty of obtaining representative cloud observations with airborne measurements but also points out how challenging it is to model polar clouds radiative effects. These radiance measurements were successfully compared to simulations, suggesting that state-of-the-art radiative transfer models are suited to study the cold and dry Arctic atmosphere. Although FIRR in situ performances compare well to its laboratory performances, complementary simulations show that upgrading the FIRR radiometric resolution would greatly increase its sensitivity to atmospheric and cloud properties. Improved instrument temperature stability in flight and expected technological progress should help meet this objective. The campaign overall highlights the potential for airborne far-infrared radiometry and constitutes a relevant reference for future similar studies dedicated to the Arctic and for the development of spaceborne instruments.

Abstract Several cloud retrieval algorithms based on satellite observations in the infrared have been developed in the last decades. However, these observations only cover the midinfrared (MIR, λ < 15 μm) part of the spectrum, and none are available in the far‐infrared (FIR, λ ≥ 15 μm). Using the optimal estimation method, we show that adding a few FIR channels to existing spaceborne radiometers would significantly improve their ability to retrieve ice cloud radiative properties. For clouds encountered in the polar regions and the upper troposphere, where the atmosphere is sufficiently transparent in the FIR, using FIR channels would reduce by more than 50% the uncertainties on retrieved values of optical thickness, effective particle diameter, and cloud top altitude. Notably, this would extend the range of applicability of current retrieval methods to the polar regions and to clouds with large optical thickness, where MIR algorithms perform poorly. The high performance of solar reflection‐based algorithms would thus be reached in nighttime conditions. Since the sensitivity of ice cloud thermal emission to effective particle diameter is approximately 5 times larger in the FIR than in the MIR, using FIR observations is a promising venue for studying ice cloud microphysics and precipitation processes. This is highly relevant for cirrus clouds and convective towers. This is also essential to study precipitation in the driest regions of the atmosphere, where strong feedbacks are at play between clouds and water vapor. The deployment in the near future of a FIR spaceborne radiometer is technologically feasible and should be strongly supported. , Plain Language Summary The size of ice cloud particles can be estimated from space by measuring the infrared emission of ice clouds. However, this method no longer works when clouds are too thick or when particles are too large, although such clouds are encountered in many regions of the Earth and are critical for the Earth's climate. Using new sensors that measure cloud emission at longer wavelengths, in the so‐called far‐infrared region, would extend the potential of satellite observations to thick clouds, to clouds made of larger particles, and to clouds found in the polar regions which are very poorly known. These new sensors have become available in the last years, but none is deployed in space so far. We show that a satellite equipped with a few channels in the far‐infrared would greatly increase our capacity to observe ice clouds. This is promising for our understanding of cloud microphysics and precipitation in general, and for studying the water cycle in the polar regions in particular. For these reasons, it is highly recommended to deploy a far‐infrared radiometer in space. , Key Points Ice cloud remote sensing would be greatly improved by adding far‐infrared channels to existing midinfrared spaceborne radiometers Far‐infrared radiometry is well suited to study ice clouds in the polar regions and upper troposphere Using far‐infrared radiometry extends current infrared capabilities to large optical thickness and large cloud particles