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The Canadian Precipitation Analysis System (CaPA) is an operational system that uses a combination of weather gauge and ground-based radar measurements together with short-term forecasts from a numerical weather model to provide near-real-time estimates of 6- and 24-h precipitation amounts. During the winter season, many gauge measurements are rejected by the CaPA quality control process because of the wind-induced undercatch for solid precipitation. The goal of this study is to improve the precipitation estimates over central Canada during the winter seasons from 2019 to 2022. Two approaches were tested. First, the quality control procedure in CaPA has been relaxed to increase the number of surface observations assimilated. Second, the automatic solid precipitation measurements were adjusted using a universal transfer function to compensate for the undercatch problem. Although increasing the wind speed threshold resulted in lower amounts and worse biases in frequency, the overall precipitation estimates are improved as the equitable threat score is improved because of a substantial decrease in the false alarm ratio, which compensates the degradation of the probability of detection. The increase of solid precipitation amounts using a transfer function improves the biases in both frequency and amounts and the probability of detection for all precipitation thresholds. However, the false alarm ratio deteriorates for large thresholds. The statistics vary from year to year, but an overall improvement is demonstrated by increasing the number of stations and adjusting the solid precipitation amounts for wind speed undercatch. [ABSTRACT FROM AUTHOR]

High-resolution, bias-corrected climate data are necessary for climate impact studies at local scales. Gridded historical data are convenient for bias correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data are generally superior climatological measurements, but because the distribution of climate stations is irregular, station data are challenging to incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980–89 and 1980–2014) and two testing sets of RCM data (1990–2014 and 1980–2014). RMSE for all methods varied throughout the year and was larger in cold months, whereas PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980–89 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method performed best in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, whereas EQM is ideal for bias correcting historical climate model output. [ABSTRACT FROM AUTHOR]

We present a novel approach to investigate variations in upper mantle and transition zone (MTZ) water content based on the joint analysis of electromagnetic (EM) signals originating in the ionosphere and magnetosphere. We invert EM signals (period range 6 hr to 85 days) to probe the electrical conductivity structure underneath 20 geomagnetic observatories, accounting for the complex spatial structure of the ionospheric and magnetospheric sources. The joint inversion of EM data for the daily and long‐period bands leads to significantly improved resolution in the upper mantle and MTZ. The conductivity profiles reveal significant lateral variability, which we interpret in terms of mantle water content by coupling electrical conductivity with constrains on mantle thermochemical structure derived from the analysis of seismic data. Our results suggest the existence of a relatively dry MTZ beneath Europe and a water‐enriched MTZ underneath North America and northern Asia. Plain Language Summary: The amount of water trapped in the Earth's interior has a strong effect on the evolution and dynamics of the planet, which ultimately controls the occurrence of earthquakes and volcanic eruptions. However, the distribution of water inside the Earth is not yet well understood. To study the Earth's deep interior, we make use of changes in the Earth's magnetic field to detect variations in electrical conductivity inside the planet. Electrical conductivity is a characteristic of a rock that varies with temperature and water content. Here, we present a novel methodology to estimate the amount of water in different regions of Earth's mantle. Our analysis suggests the presence of small amounts of water in the mantle underneath Europe, whereas larger amounts are expected beneath North America and northern Asia. Key Points: Joint inversion of daily and long‐period geomagnetic variations results in better resolved mantle conductivity structureIncorporation of seismic constraints helps isolate mantle water contentWe find a relatively dry mantle beneath Europe and a water‐enriched transition zone underneath North America and northern Asia [ABSTRACT FROM AUTHOR]

Abstract Diatom-based transfer functions for salinity, precipitation and temperature were developed using a training set that included data from 40 sites along central Mexico. These transfer functions showed good performance parameters and were subsequently applied to the previously published diatom record from lake Chalco, southern Basin of Mexico. Heinrich stadials (HS-3 to HS-0) were reconstructed as cold and dry events, with HS-2 representing the coldest and driest conditions, while HS-1 shows a complex bimodal cooling pattern. Orbital scale variability is also recorded. High and variable lake salinities from ∼34 to 29 ka BP (marine isotope stage 3, MIS-3) corresponded with times of maximum summer insolation and evidences of frequent fires. MIS-2 was identified as a cold period, with low lake salinity associated with low evaporation, with two cold intervals during the minima in spring (∼25.5 ka BP) and summer (∼20 ka BP) insolation. The ∼20 ka BP cold event together with HS-2 (24-23 ka BP), defined a bimodal maximum cooling during the last glacial maximum (LGM: 24.5–19.5 ka BP) within the range of pollen-based estimates, further supporting a maximum tropical cooling of 4–5 °C for the LGM. The deglacial (19.5–11.5 ka BP) showed the highest precipitation anomalies with three peaks that closely correlated with glacier advances in the nearby mountains (Iztaccihuatl). The early Holocene marked a change towards high lake salinities and the highest positive temperature anomalies (+3.5 °C) during a peak in summer insolation. Even though at orbital scale Chalco showed a nearly opposite trend to the record from the more southerly lake Peten-Itza (Guatemala), at millennial scale it showed a common pattern of cold and dry conditions during HSs, comparable to other tropical latitude sites in the northern hemisphere and further supporting the idea that there is a strong coupling between tropical and higher latitudes climates, in particular with the North Atlantic. Highlights • Diatom-based transfer functions produced a ∼40 ka BP paleoclimatic reconstruction for central Mexico. • Orbital and millennial scale events were identified in the salinity, precipitation and temperature reconstructions. • Maximum last glacial cooling of ∼5°C is reconstructed, a relatively wet deglacial and a warmer (+3.5°C) early Holocene. • Heinrich stadials (3 to 0) are cold and dry; HS2 was the coldest while HS-1 showed a bimodal cooling. [ABSTRACT FROM AUTHOR]

• Assisted migration (AM) is being considered to adapt forests to climate change. • We review 104 North American common-garden studies and experiments focused on AM. • Forty-seven species were investigated, but studies focused on commercial conifers. • Recent studies include local environmental variables and effects of extreme weather. • Strengths and weaknesses of studies and pathways for future research are summarized. As greenhouse gas emissions rise unabated and temperatures continue to rise, forest managers have begun to consider assisted migration (AM) of trees as a strategy to adapt forests to climate change. To investigate the risks and benefits of AM, old provenance trials have been repurposed and new common-garden experiments initiated. In this paper, we review three decades of common-garden based research into the adaptation of species and provenances (geographically specific varieties) to current and future climates in North America. Using a filtered data base of 106 papers and reports, we investigate multiple aspects of this work, including subject species, tree response variables and their climatic predictors, statistical techniques, and the patterns of adaptation revealed in results. Although the reviewed work contained valuable insights into provenance-climate relationships, much of this work focused on growth in commercial conifer species and the correlation of that growth with annual or monthly climatic averages. A minority of studies investigated the effects of climate variables with direct mechanistic effects on growth or phenology, including temperature extremes, frost and drought indices, or growing-season duration. Furthermore, recent research has opened new avenues of investigation into the influence of local soil and topographic variables, and even ectomycorrhizal communities over provenance performance. We make the case that future common-garden research into provenance-climate relationships should focus on climatic variables with demonstrated mechanistic connections to growth, survival, and phenology. The effects of extreme weather merit particular attention because extreme weather is likely to be a key determinant of long-term tree health and persistence. [ABSTRACT FROM AUTHOR]

Abstract: The need to increase the number and distribution of sea-level records spanning the last few hundred years has led to particular interest in developing high-precision, geologically based sea-level reconstructions that capture decimetre and multi-decadal scale changes. Transfer functions for tide level are statistical tools that quantify the vertical relationship between inter-tidal microfossils and elevation within the tidal frame and their use in sea-level reconstruction is growing in popularity. Whilst a range of sampling strategies, dataset qualities and underlying statistical models have been used in transfer-function development, all variants share the common requirement of accurately extracting precise species–elevation relationships from surface data, and reliably applying these to fossil assemblages to infer past conditions. We present surface foraminiferal data from six transects sampled at three sites spanning a large latitudinal range extending from Newfoundland (Canada) to North Carolina (USA). These data demonstrate that significant spatial differences exist within the high-marsh foraminiferal assemblages commonly used to reconstruct past relative sea-level (RSL). We standardise these data to account for inter-site differences in tidal range using several variants of the standardised water level index (SWLI) of Horton et al. (1999b) and show that the best performance is achieved by using the highest occurrence of foraminifera as the upper tidal datum level. The standardised surface foraminiferal data are used to develop a suite of foraminiferal transfer functions for tide level which are then applied to fossil assemblages from two sediment cores to reconstruct palaeomarsh-surface elevation. We highlight the manner in which species–elevation relationships are extracted and modified during transfer-function development, and the impacts that choices in dataset composition and transfer-function type have on the resulting reconstructions. Our results graphically illustrate the importance of these choices and the impacts of temporal and spatial variability in foraminiferal distributions, none of which are adequately represented or discernible from the standard summary statistics of performance that commonly accompany transfer-function reconstructions. We conclude that a more explicit treatment of the transfer-function development process is required to support the growing body of precise RSL reconstructions that are now appearing in the literature. To that end, we make the following four recommendations for a framework to assist in transfer-function development that will allow better record inter-comparison and minimise the potential for producing precise but ultimately inaccurate reconstructions: 1) sampling for surface data should focus on capturing complete species response curves rather than simply compiling modern analogues for fossil material; 2) The similarity between the surface assemblages used to produce transfer functions and the fossil assemblages to which they are applied, should be better quantified using a range of statistical approaches, and must accompany any reconstruction; 3) where modern analogues are lacking in local surface assemblages, data from additional sites should be added, with selection based on fulfilling the requirements of the previous two recommendations. The manner in which individual species optima are combined to produce a composite optimum, and the extent to which this provides a reasonable representation of the surface data should also be documented; 4) the application of WA PLS component 2 (and higher) cannot be justified solely on the grounds of increased performance measures (e.g. r2 jack and RMSEP), and if applied, the pattern of optima updates should be used to highlight species that may distort reconstructions. [Copyright &y& Elsevier]

The relationships between diatom assemblages and important limnological variables were investigated in 494 lakes from northeastern North America (Pennsylvania, USA, to Nova Scotia, Canada). The limnological variable most significantly related to diatom assemblages was lake water pH, although dissolved organic carbon and nutrients were also important. Based on these strong relationships, highly significant diatom-based inference models were developed to reconstruct key limnological variables based on diatom assemblages using weighted averaging (WA), maximum likelihood (ML), and modern analogs technique (MAT). The performances of the pH-inference models were high, similar, and significant (WA: r2boot = 0.89, root mean squared error of prediction (RMSEP) = 0.43; ML: r2boot = 0.89, RMSEP = 0.45; MAT: r2boot = 0.89, RMSEP = 0.46). In addition, distribution of sites along a pH gradient did not have the anticipated bias, especially with respect to the WA model, although an evenly distributed study set did result in slightly less noise. While some regionally specific information may be lost by utilizing a large number of lakes from a wide geographic area, the broad limnological gradients allow a more realistic, accurate, and complete description of the ecological characteristics of diatom species. In addition to providing new autecological data on diatoms for northeastern North America, these inference models can now be used to infer accurately and precisely lake water pH and associated variables for limnologists and lake managers. [ABSTRACT FROM AUTHOR]

The proliferation of quantifications of past climate parameters from fossil assemblage data in the past decades has improved our understanding of past climate variability. However, our knowledge has been restricted to a few regions, such as North America and parts of Eurasia. In addition, even in these highly studied regions, strong regional discrepancies between reconstructions have been found, which suggests that some pieces of the puzzle are still missing. We argue that these discrepancies in the modern set of reconstructions can be explained by the lack of consideration given to uncertainties, such as chronological or climatic uncertainties. Most reconstructions are usually presented and interpreted only according to the 'most probable scenario', a scenario defined by associating one certain age and one certain climate value to each sample. These simplifications usually hinder robust comparisons of palaeorecords built on different timescales and/or have different temporal resolutions.The new statistical transfer function we have developed, entitled CREST for Climate REconstruction SofTware, proposes a new global solution regarding these two issues. CREST uses probability density functions ('pdfs') fitted on modern occurrence data to reconstruct environmental parameters from fossil assemblage data (e.g. pollen). It offers many advantages over the classical approaches, such as 1) a higher flexibility of application, 2) a global applicability on large datasets, 3) a compatibility with a large range of proxy data, and most importantly, 4) a higher capacity to estimate and use uncertainties. Through the development of a fully probabilistic process that conserves and combines all the uncertainties at every stage of the process – instead of trying to reduce them at all cost – the CREST framework can produce 3-dimensional posterior distributions of environmental parameters on error-free timescales; thus facilitating the comparison of paleorecords with different chronologies, and also with instrumental data and climate model outputs. In this paper, we present the key aspects of this framework, and illustrate its potential by showcasing temperature and rainfall reconstructions derived from African pollen data, and the key role they played in changing our understanding of the glacial-interglacial dynamic of the African rainbelt. [ABSTRACT FROM AUTHOR]