CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool

The CICERO Simple Climate Model (CICERO-SCM) is a lightweight, semi-empirical model of global climate. Here we present a new open-source Python port of the model for use in climate assessment and research. The new version of CICERO-SCM has the same scientific logic and functionality as the original Fortran version, but it is considerably more flexible and also open-source via GitHub. We describe the basic structure and improvements compared to the previous Fortran version, together with technical descriptions of the global thermal dynamics and carbon cycle components and the emission module, before presenting a range of standard figures demonstrating its application. A new parameter calibration tool is demonstrated to make an example calibrated parameter set to span and fit a simple target specification. CICERO-SCM is fully open-source and available through GitHub (https://github.com/ciceroOslo/ciceroscm, last access: 23 August 2024).</p

Scientific data from precipitation driver response model intercomparison project

This data descriptor reports the main scientific values from General Circulation Models (GCMs) in the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). The purpose of the GCM simulations has been to enhance the scientific understanding of how changes in greenhouse gases, aerosols, and incoming solar radiation perturb the Earth's radiation balance and its climate response in terms of changes in temperature and precipitation. Here we provide global and annual mean results for a large set of coupled atmospheric-ocean GCM simulations and a description of how to easily extract files from the dataset. The simulations consist of single idealized perturbations to the climate system and have been shown to achieve important insight in complex climate simulations. We therefore expect this data set to be valuable and highly used to understand simulations from complex GCMs and Earth System Models for various phases of the Coupled Model Intercomparison Project

Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response

Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1 ∘C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2 ∘C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3 ∘C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23∘C(±0.12 ∘C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs

Updated global warming potentials and radiative efficiencies of halocarbons and other weak atmospheric absorbers

Human activity has led to increased atmospheric concentrations of many gases, including halocarbons, and may lead to emissions of many more gases. Many of these gases are, on a per molecule basis, powerful greenhouse gases, although at present‐day concentrations their climate effect is in the so‐called weak limit (i.e. their effect scales linearly with concentration). We published a comprehensive review of the radiative efficiencies (RE) and global warming potentials (GWP) for around 200 such compounds in 2013 (Hodnebrog et al., 2013). Here we present updated RE and GWP values for compounds where experimental infrared absorption spectra are available. Updated numbers are based on a revised “Pinnock curve”, which gives RE as a function of wavenumber, and now also accounts for stratospheric temperature adjustment (Shine and Myhre, 2020). Further updates include the implementation of around 500 absorption spectra additional to those in the 2013 review, and new atmospheric lifetimes from the literature (mainly from WMO (2019)). In total, values for 60 of the compounds previously assessed are based on additional absorption spectra, and 42 compounds have REs which differ by >10% from our previous assessment. New RE calculations are presented for more than 400 compounds in addition to the previously assessed compounds, and GWP calculations are presented for a total of around 250 compounds. Present‐day radiative forcing due to halocarbons and other weak absorbers is 0.38 [0.33‐0.43] W m‐2, compared to 0.36 [0.32‐0.40] W m‐2 in IPCC AR5 (Myhre et al., 2013), which is about 18% of the current CO2 forcing

EGFR, CD10 and proliferation marker Ki67 expression in ameloblastoma: possible role in local recurrence

<p>Abstract</p> <p>Background</p> <p>Ameloblastoma is an odontogenic neoplasm characterized by local invasiveness and tendency towards recurrence.</p> <p>Aims</p> <p>Studying the role played by EGFR, CD10 and Ki67 in the recurrence of ameloblastoma.</p> <p>Methods</p> <p>This study was carried out on 22 retrospective cases of mandibular ameloblastoma from the period from Jan 2002 to Jan 2008 with follow up period until Jan 2011 (3 to 8 years follow up peroid). Archival materials were obtained from pathology department, Mansoura university. Paraffin sections of tumor tissue from all cases were submitted for routine H&E stains and immunohistochemistry using EGFR, CD10 and Ki67 monoclonal antibodies. Statistical analysis using of clinical data for all patients, tumor type, EGFR, CD10 and Ki67 expression in relation to recurrence were evaluated.</p> <p>Results</p> <p>Among the 22 cases, 10 cases were males and 12 were females with sex ratio 1:1.2. Age ranged from 34 to 59 years old with a mean age 44.18 year. Five cases showed local recurrence within studied period and proved by biopsy. No statistically significant relation was found between local recurrence and patient age, tumor size, tumor type, EGFR expression. There was a significant relation between CD10 expression as well as Ki67 labelling index and recurrence (P value = 0.003, 0.000 respectively).</p> <p>Conclusion</p> <p>Evaluation of CD10 and Ki67 status together with conventional histological evaluation can help in providing more information about the biologic behavior of the tumor, while EGFR could be a target of an expanding class of anticancer therapies.</p> <p>Since ameloblastomas are EGFR-positive tumors, anti-EGFR agents could be considered to reduce the size of large tumors and to treat unresectable tumors that are in close proximity to vital structures.</p> <p>Virtual Slides</p> <p>The virtual slide(s) for this article can be found here:</p> <p><url>http://www.diagnosticpathology.diagnomx.eu/vs/1902106905645651</url></p

Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections

Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections. Plain Language Summary Our best tools to capture state-of-the-art knowledge are complex, fully coupled Earth System Models (ESMs). However, ESMs are expensive to run and no single ESM can easily produce responses which represent the full range of uncertainties. Instead, for some applications, computationally efficient reduced complexity climate models (RCMs) are used in a probabilistic setup. An example of these applications is estimating the likelihood that an emissions scenario will stay below a certain global-mean temperature change. Here we present a study (referred to as the Reduced Complexity Model Intercomparison Project (RCMIP) Phase 2) which investigates the extent to which different RCMs can be probabilistically calibrated to reproduce knowledge from specialized research communities. We find that the agreement between each RCM and the benchmarks varies, although the best performing models show good agreement across the majority of benchmarks. Under a very-low-emissions scenario median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, assuming historical warming of 0.8°C between 1850–1900 and 1995–2014). Investigating new ways to reduce these projection uncertainties seems paramount given the international community's goal to limit warming to below 1.5°C above preindustrial in the long-term