Observational Constraints On The Influence Of A...
Observations facilitate model evaluation and provide constraints that are relevant to future predictions and projections. Constraints for uninitialized projections are generally based on model performance in simulating climatology and climate change. For initialized predictions, skill scores over the hindcast period provide insight into the relative performance of models, and the value of initialization as compared to projections. Predictions and projections combined can, in principle, provide seamless decadal to multi-decadal climate information. For that, though, the role of observations in skill estimates and constraints needs to be understood in order to use both consistently across the prediction and projection time horizons. This paper discusses the challenges in doing so, illustrated by examples of state-of-the-art methods for predicting and projecting changes in European climate. It discusses constraints across prediction and projection methods, their interpretation, and the metrics that drive them such as process accuracy, accurate trends or high signal-to-noise ratio. We also discuss the potential to combine constraints to arrive at more reliable climate prediction systems from years to decades. To illustrate constraints on projections, we discuss their use in the UK's climate prediction system UKCP18, the case of model performance weights obtained from the Climate model Weighting by Independence and Performance (ClimWIP) method, and the estimated magnitude of the forced signal in observations from detection and attribution. For initialized predictions, skill scores are used to evaluate which models perform well, what might contribute to this performance, and how skill may vary over time. Skill estimates also vary with different phases of climate variability and climatic conditions, and are influenced by the presence of external forcing. This complicates the systematic use of observational constraints. Furthermore, we illustrate that sub-selecting simulations from large ensembles based on reproduction of the observed evolution of climate variations is a good testbed for combining projections and predictions. Finally, the methods described in this paper potentially add value to projections and predictions for users, but must be used with caution.
Observational Constraints on the Influence of A...
Figure 1. Examples of the impact of constraints derived from historical climatology (Hist), added historical global surface air temperature trends (Hist+SAT), added historical trends in atmospheric CO2 concentration (Hist+SAT+CO2), and added upper ocean heat content (Hist+SAT+CO2+OHC), in modifying the prior distribution to form the posterior. The 5th, 50th (median), and 95th percentiles are plotted, along with the pdfs. Results prior to the application of observational constraints (Prior with discrepancy) are also shown. Reproduced from Murphy et al. (2018).
Both surface air temperature and ocean heat content continually increase in the historical period in response to greenhouse gas forcing in historical greenhouse gas only simulations (figures 1(a), (b)). As a result, there is an approximately linear relationship between the greenhouse gas attributable temperature and ocean heat content, shown in figure 1(c). We make use of this emergent property of the climate system in response to greenhouse gases alone later in section 3.4 to provide observational constraints on the historical greenhouse gas attributable responses. (Note that models with high atmospheric warming do not necessarily have larger increases in ocean heat content, for the periods considered here.)
Such lower values for S_hist_GHG than S at equilibrium can be explained by the effects of changing strength of the feedbacks at higher levels of warming (Knutti et al 2017). The climate feedback parameter (defined as 1/S_hist) has been shown to vary in the historical period (Gregory and Andrews 2016, Andrews et al 2018), and depends on both time-variation and the forcing agent. Andrews et al 2018 show that the feedback parameter (1/S_hist) is decreasing, particularly after 1940s onwards (Andrews et al 2018; figure 2(f) therein). This would suggest increase in S_hist\,\,during that period. Potential changes in feedbacks are neglected if assuming that S_hist is equal to S (at equilibrium), an assumption often made if inferring S\,\,using simple climate models with constant feedbacks. Our tighter and lower values for S_hist_GHG may be affected by this effect, but may alternatively also reflect better constraints with a longer time horizon, or be affected by uncertainties in the early record we can not fully quantify. For example, an uncertainty in the long period is that it effectively uses extrapolation of the observational constraint from the second half of the 20th century to the full analysis period, which may introduce error particularly if some model simulations are affected by drift in the ocean. Also, analysis periods can matter both due to effects of internal climate variability and possibly residuals from responses by other forcings that may have been not fully separated in the attribution analysis.
In fields such as epidemiology, social sciences, psychology and statistics, an observational study draws inferences from a sample to a population where the independent variable is not under the control of the researcher because of ethical concerns or logistical constraints. One common observational study is about the possible effect of a treatment on subjects, where the assignment of subjects into a treated group versus a control group is outside the control of the investigator. This is in contrast with experiments, such as randomized controlled trials, where each subject is randomly assigned to a treated group or a control group. Observational studies, for lacking an assignment mechanism, naturally present difficulties for inferential analysis.
In all of those cases, if a randomized experiment cannot be carried out, the alternative line of investigation suffers from the problem that the decision of which subjects receive the treatment is not entirely random and thus is a potential source of bias. A major challenge in conducting observational studies is to draw inferences that are acceptably free from influences by overt biases, as well as to assess the influence of potential hidden biases. The following are a non-exhaustive set of problems especially common in observational studies.
We discuss constraints on cosmic reionisation and their implications on acosmic SFR density $\rho_\mathrmSFR$ model; we study the influence ofkey-parameters such as the clumping factor of ionised hydrogen in theintergalactic medium (IGM) $C_H_II$ and the fraction of ionising photonsescaping star-forming galaxies to reionise the IGM $f_\mathrmesc$. Ouranalysis uses SFR history data coming from luminosity functions, assuming thatstar-forming galaxies were sufficient to lead the reionisation process at highredshift. We add two other sets of constraints: measurements of the IGM ionisedfraction and the most recent result from Planck Satellite about the integratedThomson optical depth of the Cosmic Microwave Background (CMB)$\tau_\mathrmPlanck$. We also consider various possibilities for theevolution of these two parameters with redshift, and confront them withobservational data cited above. We conclude that, if the model of a constantclumping factor is chosen, the fiducial value of $3$ often used in papers isconsistent with observations; even if a redshift-dependent model is considered,the resulting optical depth is strongly correlated to $C_H_II$ mean valueat $z>7$, an additional argument in favour of the use of a constant clumpingfactor. Besides, the escape fraction is related to too many astrophysicalparameters to allow us to use a complete and fully satisfactory model. Aconstant value with redshift seems again to be the most likely expression:considering it as a fit parameter, we get from the maximum likelihood (ML)model $f_\mathrmesc=0.24\pm0.08$; with a redshift-dependent model, we find analmost constant evolution, slightly increasing with $z$, around$f_\mathrmesc=0.23$. Last, our analysis shows that a reionisation beginningas early as $z\geq14$ and persisting until $z\sim6$ is a likely storyline.
Science goal and objectives: We propose to measure the flows from the surface down to the upper shear layer in the convection zone around 5% below the surface and provide observational constraints for the latitudinal, longitudinal, and temporal variations of the flow components. The goal is to establish the extent to which these large-scale flows drive the emergence and evolution of magnetic flux at the solar surface and the subsequent poleward flux transport. This will lead to a better understanding of the generation and evolution of global-scale magnetic fields.
Proposed Contributions to the Focus Team Effort: The proposed study of the large-scale flows in the upper convection zone will provide observational constraints, which will be crucial for the modeling of the solar interior and the solar flux transport. This will lead to a better understanding of the generation and evolution of global-scale magnetic fields and, as a consequence, provide constraints for forecasting solar activity. The proposed study therefore addresses the objective of Focused Science Topic (4): Understanding Global-scale Solar Processes and their Implications for the Solar Interior. 041b061a72